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Paper Digest: Recent Papers on Deep Fake

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Paper Digest Team extracted all recent Deep Fake related papers on our radar, and generated highlight sentences for them. The results are then sorted by relevance & date. In addition to this ‘static’ page, we also provide a real-time version of this article, which has more coverage and is updated in real time to include the most recent updates on this topic.

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TABLE 1: Paper Digest: Recent Papers on Deep Fake

Paper Author(s) Source Date
1 A Dataless FaceSwap Detection Approach Using Synthetic Images
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Highlight: We propose a deepfake detection methodology that eliminates the need for any real data by making use of synthetically generated data using StyleGAN3.		 Anubhav Jain; Nasir Memon; Julian Togelius; arxiv-cs.CV 2022-12-05
2 FakeOut: Leveraging Out-of-domain Self-supervision for Multi-modal Video Deepfake Detection
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Highlight: Thus, there is a pressing need for accurate and robust deepfake detection methods, that can detect forgery techniques not seen during training. In this work, we explore whether this can be done by leveraging a multi-modal, out-of-domain backbone trained in a self-supervised manner, adapted to the video deepfake domain.		 Gil Knafo; Ohad Fried; arxiv-cs.CV 2022-12-01
3 Deep Fake Detection, Deterrence and Response: Challenges and Opportunities
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Highlight: Afterward, we offer a solution that is capable of 1) making our AI systems robust against deepfakes during development and deployment phases; 2) detecting video, image, audio, and textual deepfakes; 3) identifying deepfakes that bypass detection (deepfake hunting); 4) leveraging available intelligence for timely identification of deepfake campaigns launched by state-sponsored hacking teams; 5) conducting in-depth forensic analysis of identified deepfake payloads.		 Amin Azmoodeh; Ali Dehghantanha; arxiv-cs.CR 2022-11-26
4 Deepfake Detection Via Joint Unsupervised Reconstruction and Supervised Classification
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Highlight: In addition, the reconstruction task can improve the learned representations. In this paper, we introduce a novel approach for deepfake detection, which considers the reconstruction and classification tasks simultaneously to address these problems.		 Bosheng Yan; Xuequan Lu; Chang-Tsun Li; arxiv-cs.CV 2022-11-24
5 Protecting World Leaders Against Deep Fakes Using Facial, Gestural, and Vocal Mannerisms
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Highlight: We describe an identity-based approach for protecting world leaders from deep-fake imposters.		 Matyáš Boháček; Hany Farid; Proceedings of the National Academy of Sciences of the … 2022-11-23
6 Deepfake Detection: A Comprehensive Study from The Reliability Perspective
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Highlight: In this paper, we provide a thorough review of the existing models following the development history of the Deepfake detection studies and define the research challenges of Deepfake detection in three aspects, namely, transferability, interpretability, and reliability.		 Tianyi Wang; Kam Pui Chow; Xiaojun Chang; Yinglong Wang; arxiv-cs.CV 2022-11-20
7 Cross-Domain Local Characteristic Enhanced Deepfake Video Detection
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Highlight: To this end, we propose a novel pipeline, Cross-Domain Local Forensics (XDLF), for more general deepfake video detection.		 Zihan Liu; Hanyi Wang; Shilin Wang; arxiv-cs.CV 2022-11-07
8 Delving Into Sequential Patches for Deepfake Detection
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Highlight: Based on low-level temporal inconsistency understanding, we identify deepfake videos in a more robust and generalizable way with model designs in a Transfomer style.		 JIAZHI GUAN et. al. nips 2022-11-06
9 OST: Improving Generalization of DeepFake Detection Via One-Shot Test-Time Training
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Highlight: In this paper, we introduce a new learning paradigm specially designed for the generalizable deepfake detection task.		 Liang Chen; Yong Zhang; Yibing Song; Jue Wang; Lingqiao Liu; nips 2022-11-06
10 DeepFake Knee Osteoarthritis X-rays from Generative Adversarial Neural Networks Deceive Medical Experts and Offer Augmentation Potential to Automatic Classification
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Highlight: This study presents generative adversarial neural networks capable of generating realistic images of knee joint X-rays with varying osteoarthritis severity.		 Fabi Prezja; Juha Paloneva; Ilkka Pölönen; Esko Niinimäki; Sami Äyrämö; Scientific reports 2022-11-03
11 Waveform Boundary Detection for Partially Spoofed Audio
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Highlight: The present paper proposes a waveform boundary detection system for audio spoofing attacks containing partially manipulated segments.		 Zexin Cai; Weiqing Wang; Ming Li; arxiv-eess.AS 2022-10-31
12 AI-Generated Face Image Identification with Different Color Space Channel Combinations
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Abstract: With the rapid development of the Internet and information technology (in particular, generative adversarial networks and deep learning), network data are exploding. Due to the …		 Songwen Mo; Pei Lu; Xiaoyong Liu; Sensors (Basel, Switzerland) 2022-10-27
13 Exploring Spatial-Temporal Features for Deepfake Detection and Localization
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Highlight: In this work, we propose a Spatial-Temporal Deepfake Detection and Localization (ST-DDL) network that simultaneously explores spatial and temporal features for detecting and localizing forged regions.		 Wu Haiwei; Zhou Jiantao; Zhang Shile; Tian Jinyu; arxiv-cs.CV 2022-10-27
14 DEEPFAKE CLI: Accelerated Deepfake Detection Using FPGAs
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Highlight: In this paper, we have used light MesoNet with EFF-YNet structure and accelerated it on VCK5000 FPGA, powered by state-of-the-art VC1902 Versal Architecture which uses AI, DSP, and Adaptable Engines for acceleration.		 OMKAR BHILARE et. al. arxiv-cs.AR 2022-10-26
15 Towards A Robust Deepfake Detector:Common Artifact Deepfake Detection Model
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Highlight: In this paper, we propose a novel deepfake detection method named Common Artifact Deepfake Detection Model, which aims to learn common artifact features in different face manipulation algorithms.		 SHICHAO DONG et. al. arxiv-cs.CV 2022-10-26
16 Using A Visual Turing Test to Evaluate The Realism of Generative Adversarial Network (GAN)-Based Synthesized Myocardial Perfusion Images
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Abstract: As the quality of image generation by deep learning increases, it is becoming difficult to discern its authenticity from the image alone. Currently, generative models represented …		 Akinori Higaki; Yoshitaka Kawada; Go Hiasa; Tadakatsu Yamada; Hideki Okayama; Cureus 2022-10-24
17 Adaptive Re-calibration of Channel-wise Features for Adversarial Audio Classification
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Highlight: DeepFake Audio, unlike DeepFake images and videos, has been relatively less explored from detection perspective, and the solutions which exist for the synthetic speech classification either use complex networks or dont generalize to different varieties of synthetic speech obtained using different generative and optimization-based methods. Through this work, we propose a channel-wise recalibration of features using attention feature fusion for synthetic speech detection and compare its performance against different detection methods including End2End models and Resnet-based models on synthetic speech generated using Text to Speech and Vocoder systems like WaveNet, WaveRNN, Tactotron, and WaveGlow.		 Vardhan Dongre; Abhinav Thimma Reddy; Nikhitha Reddeddy; arxiv-cs.SD 2022-10-21
18 Deepfake Attack Prevention Using Steganography GANs
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Abstract: Deepfakes are fake images or videos generated by deep learning algorithms. Ongoing progress in deep learning techniques like auto-encoders and generative adversarial networks …		 Iram Noreen; Muhammad Shahid Muneer; Saira Gillani; PeerJ. Computer science 2022-10-20
19 Adaptive Face Forgery Detection in Cross Domain
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Highlight: In addition to this, the inconsistency problem in the previous methods is significantly exacerbated due to the diversities among various forgery methods. To address this problem, we propose a novel deep learning framework for face forgery detection in cross domain.		 LUCHUAN SONG et. al. eccv 2022-10-19
20 Detecting and Recovering Sequential DeepFake Manipulation
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Highlight: This new threat requires us to detect a sequence of facial manipulations, which is vital for both detecting deepfake media and recovering original faces afterwards. Motivated by this observation, we emphasize the need and propose a novel research problem called Detecting Sequential DeepFake Manipulation (Seq-DeepFake).		 Rui Shao; Tianxing Wu; Ziwei Liu; eccv 2022-10-19
21 Explaining Deepfake Detection By Analysing Image Matching
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Highlight: This paper aims to interpret how deepfake detection models learn artifact features of images when just supervised by binary labels.		 Shichao Dong; Jin Wang; Jiajun Liang; Haoqiang Fan; Renhe Ji; eccv 2022-10-19
22 Hierarchical Contrastive Inconsistency Learning for Deepfake Video Detection
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Highlight: In this paper, we propose a novel Hierarchical Contrastive Inconsistency Learning framework (HCIL) with a two-level contrastive paradigm.		 Zhihao Gu; Taiping Yao; Yang Chen; Shouhong Ding; Lizhuang Ma; eccv 2022-10-19
23 Do Deepfakes Adequately Display Emotions? A Study on Deepfake Facial Emotion Expression
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Highlight: In this work, we conduct an in-depth study on facial emotional expression in deepfakes using a well-known face swap-based deepfake database.		 Juan-Miguel López-Gil; Rosa Gil; Roberto García; Computational intelligence and neuroscience 2022-10-18
24 Deepfake Text Detection: Limitations and Opportunities
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Highlight: In this paper, we collect deepfake text from 4 online services powered by Transformer-based tools to evaluate the generalization ability of the defenses on content in the wild.		 JIAMENG PU et. al. arxiv-cs.CR 2022-10-17
25 Deepfake Detection System for The ADD Challenge Track 3.2 Based on Score Fusion
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Highlight: This paper describes the deepfake audio detection system submitted to the Audio Deep Synthesis Detection (ADD) Challenge Track 3.2 and gives an analysis of score fusion.		 YUXIANG ZHANG et. al. arxiv-eess.AS 2022-10-13
26 SpecRNet: Towards Faster and More Accessible Audio DeepFake Detection
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Highlight: In this work, we focus on increasing accessibility to the audio DeepFake detection methods by providing SpecRNet, a neural network architecture characterized by a quick inference time and low computational requirements.		 Piotr Kawa; Marcin Plata; Piotr Syga; arxiv-cs.SD 2022-10-12
27 Gotcha: A Challenge-Response System for Real-Time Deepfake Detection
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Highlight: This paper presents Gotcha, a real-time deepfake detection system for live video interactions.		 Govind Mittal; Jiraphon Yenphraphai; Chinmay Hegde; Nasir Memon; arxiv-cs.CR 2022-10-12
28 Deep Fake Video Detection Using Transfer Learning Approach
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Highlight: However, it is more critical to detect fake content because of the increased complexity of leveraging various approaches to smudge the tampering. Therefore, this work proposes a novel framework to detect fake videos through the utilization of transfer learning in autoencoders and a hybrid model of convolutional neural networks (CNN) and Recurrent neural networks (RNN).		 Shraddha Suratkar; Faruk Kazi; Arabian journal for science and engineering 2022-10-11
29 Aggregating Layers for Deepfake Detection
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Highlight: We evaluate our approach on two domains of interest – Deepfake detection and Synthetic image detection, and find that we achieve SOTA results.		 Amir Jevnisek; Shai Avidan; arxiv-cs.CV 2022-10-11
30 Quantitative Metrics for Evaluating Explanations of Video DeepFake Detectors
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Highlight: We describe a simple set of metrics to evaluate the visual quality and informativeness of explanations of video DeepFake classifiers from a human-centric perspective.		 Federico Baldassarre; Quentin Debard; Gonzalo Fiz Pontiveros; Tri Kurniawan Wijaya; arxiv-cs.CV 2022-10-07
31 Evaluation of Pre-Trained CNN Models for Geographic Fake Image Detection
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Highlight: To advance the field, in this paper, we explore the suitability of several convolutional neural network (CNN) architectures for fake satellite image detection.		 Sid Ahmed Fezza; Mohammed Yasser Ouis; Bachir Kaddar; Wassim Hamidouche; Abdenour Hadid; arxiv-cs.CV 2022-10-01
32 The Face Deepfake Detection Challenge
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Abstract: Multimedia data manipulation and forgery has never been easier than today, thanks to the power of Artificial Intelligence (AI). AI-generated fake content, commonly called …		 LUCA GUARNERA et. al. Journal of imaging 2022-09-28
33 When Handcrafted Features and Deep Features Meet Mismatched Training and Test Sets for Deepfake Detection
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Highlight: In this paper, we utilize and compare two kinds of handcrafted features(SIFT and HoG) and two kinds of deep features(Xception and CNN+RNN) for the deepfake detection task.		 Ying Xu; Sule Yildirim Yayilgan; arxiv-cs.CV 2022-09-27
34 DeePhy: On Deepfake Phylogeny
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Highlight: Model attribution helps in enhancing the explainability of the detection results by providing information on the generative model employed. In order to enable the research community to address these questions, this paper proposes DeePhy, a novel Deepfake Phylogeny dataset which consists of 5040 deepfake videos generated using three different generation techniques.		 KARTIK NARAYAN et. al. arxiv-cs.CV 2022-09-19
35 TIMIT-TTS: A Text-to-Speech Dataset for Multimodal Synthetic Media Detection
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Highlight: In this paper we focus on the generation of an audio-visual deepfake dataset.		 Davide Salvi; Brian Hosler; Paolo Bestagini; Matthew C. Stamm; Stefano Tubaro; arxiv-cs.MM 2022-09-16
36 Detecting Synthetic Speech Manipulation in Real Audio Recordings
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Highlight: This paper describes a potential partial synthetic speech detection system based on the x-ResNet architecture with a probabilistic linear discriminant analysis (PLDA) backend and interleaved aware score processing.		 MD HAFIZUR RAHMAN et. al. arxiv-cs.SD 2022-09-15
37 Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique
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Highlight: This study aims at improving the detection accuracy by proposing a deep ensemble fake news detection model using the sequential deep learning technique.		 Abdullah Marish Ali; Fuad A Ghaleb; Bander Ali Saleh Al-Rimy; Fawaz Jaber Alsolami; Asif Irshad Khan; Sensors (Basel, Switzerland) 2022-09-15
38 DeepFake Detection Based on Discrepancies Between Faces and Their Context
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Abstract: We propose a method for detecting face swapping and other identity manipulations in single images. Face swapping methods, such as DeepFake, manipulate the face region, aiming to …		 Yuval Nirkin; Lior Wolf; Yosi Keller; Tal Hassner; IEEE transactions on pattern analysis and machine … 2022-09-14
39 Efficient Low-thrust Trajectory Data Generation Based on Generative Adversarial Network
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Highlight: Deep learning-based techniques have been introduced into the field of trajectory optimization in recent years.		 Ruida Xie; Andrew G. Dempster; arxiv-cs.LG 2022-09-14
40 A Natural Language Processing (NLP) Evaluation on COVID-19 Rumour Dataset Using Deep Learning Techniques
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Highlight: The study’s objective is to propose a novel solution to detect fake news using state-of-the-art machines and deep learning models.		 RUBIA FATIMA et. al. Computational intelligence and neuroscience 2022-09-14
41 Evaluation of Deepfake Detection Using YOLO with Local Binary Pattern Histogram
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Abstract: Recently, deepfake technology has become a popularly used technique for swapping faces in images or videos that create forged data to mislead society. Detecting the originality of …		 Štěpán Hubálovský; Pavel Trojovský; Nebojsa Bacanin; Venkatachalam K; PeerJ. Computer science 2022-09-13
42 Deep Convolutional Pooling Transformer for Deepfake Detection
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Highlight: Therefore, the existing Deepfake detection methods have reached a bottleneck to further improving the detection performance. To address this issue, we propose a deep convolutional Transformer to incorporate the decisive image features both locally and globally.		 Tianyi Wang; Harry Cheng; Kam Pui Chow; Liqiang Nie; arxiv-cs.CV 2022-09-12
43 Multimodal Fake-News Recognition Using Ensemble of Deep Learners
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Abstract: Social networks have drastically changed how people obtain information. News in social networks is accompanied by images and videos and thus receives more attention from readers …		 Abdulhameed Al Obaid; Hassan Khotanlou; Muharram Mansoorizadeh; Davood Zabihzadeh; Entropy (Basel, Switzerland) 2022-09-03
44 DepthFake: A Depth-based Strategy for Detecting Deepfake Videos
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Highlight: In this paper, we proposed DepthFake, a study on how to improve classical RGB-based approaches with depth-maps.		 Luca Maiano; Lorenzo Papa; Ketbjano Vocaj; Irene Amerini; arxiv-cs.CV 2022-08-23
45 Initial Development of Perpetrator Confrontation Using Deepfake Technology in Victims with Sexual Violence-related PTSD and Moral Injury
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Abstract: Interventions aimed at easing negative moral (social) emotions and restoring social bonds – such as amend-making and forgiving-have a prominent role in the treatment of moral …		 AGNES VAN MINNEN et. al. Frontiers in psychiatry 2022-08-18
46 DF-Captcha: A Deepfake Captcha for Preventing Fake Calls
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Highlight: In this paper, we propose a lightweight application which can protect organizations and individuals from deepfake SE attacks.		 Yisroel Mirsky; arxiv-cs.CR 2022-08-17
47 Deepfake Detection Using ImageNet Models and Temporal Images of 468 Facial Landmarks
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Highlight: This paper presents our results and findings on the use of temporal images for deepfake detection.		 Christeen T Jose; arxiv-cs.CV 2022-08-14
48 A PNU-Based Methodology to Improve The Reliability of Biometric Systems
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Abstract: Face recognition is an important application of pattern recognition and image analysis in biometric security systems. The COVID-19 outbreak has introduced several issues that can …		 Paola Capasso; Lucia Cimmino; Andrea F Abate; Andrea Bruno; Giuseppe Cattaneo; Sensors (Basel, Switzerland) 2022-08-14
49 A Comprehensive Analysis of AI Biases in DeepFake Detection With Massively Annotated Databases
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Highlight: However, there is an increased concern that these models and training databases might be biased and thus, cause DeepFake detectors to fail. In this work, we tackle these issues by (a) providing large-scale demographic and non-demographic attribute annotations of 41 different attributes for five popular DeepFake datasets and (b) comprehensively analysing AI-bias of multiple state-of-the-art DeepFake detection models on these databases.		 Ying Xu; Philipp Terhörst; Kiran Raja; Marius Pedersen; arxiv-cs.CV 2022-08-11
50 Hybrid Transformer Network for Deepfake Detection
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Highlight: In this study, we propose a novel hybrid transformer network utilizing early feature fusion strategy for deepfake video detection.		 Sohail Ahmed Khan; Duc-Tien Dang-Nguyen; arxiv-cs.CV 2022-08-11
51 Study of Detecting Behavioral Signatures Within DeepFake Videos
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Highlight: There is strong interest in the generation of synthetic video imagery of people talking for various purposes, including entertainment, communication, training, and advertisement.		 QIAOMU MIAO et. al. arxiv-cs.CV 2022-08-06
52 Audio Deepfake Detection Based on A Combination of F0 Information and Real Plus Imaginary Spectrogram Features
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Highlight: Recently, pioneer research works have proposed a large number of acoustic features (log power spectrogram, linear frequency cepstral coefficients, constant Q cepstral coefficients, etc.) for audio deepfake detection, obtaining good performance, and showing that different subbands have different contributions to audio deepfake detection.		 JUN XUE et. al. arxiv-cs.SD 2022-08-01
53 Investigation of Craquelure Patterns in Oil Paintings Using Precise 3D Morphological Analysis for Art Authentication
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Abstract: The development of scientific technology for art authentication has elicited multidimensional evidence to distinguish forgeries from original artwork. Here, we analyzed the …		 SOOJUNG KIM et. al. PloS one 2022-07-28
54 Modelling Social Context for Fake News Detection: A Graph Neural Network Based Approach
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Highlight: Our recent studies find that the propagation characteristics of authentic and fake news are distinguishable, irrespective of their modalities. In this regard, we have investigated the auxiliary information based on social context to detect fake news.		 PALLABI SAIKIA et. al. arxiv-cs.SI 2022-07-27
55 Using Deep Learning to Detecting Deepfakes
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Highlight: This enables them to mimic a power figure in society, such as a president or celebrity, creating the potential danger of spreading misinformation and other nefarious uses of deepfakes. To combat this online threat, researchers have developed models that are designed to detect deepfakes.		 Jacob Mallet; Rushit Dave; Naeem Seliya; Mounika Vanamala; arxiv-cs.CV 2022-07-27
56 Detecting Deepfake By Creating Spatio-Temporal Regularity Disruption
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Highlight: Specifically, by carefully examining the spatial and temporal properties, we propose to disrupt a real video through a Pseudo-fake Generator and create a wide range of pseudo-fake videos for training.		 JIAZHI GUAN et. al. arxiv-cs.CV 2022-07-21
57 GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection
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Highlight: Several solutions have been proposed by the vision community to effectively combat the misinformation on the internet via automated deepfake detection systems.		 Aakash Varma Nadimpalli; Ajita Rattani; arxiv-cs.CV 2022-07-20
58 Deep Fake Detection Using Cascaded Deep Sparse Auto-encoder for Effective Feature Selection
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Highlight: This paper has developed a Deepfake detection method by examining the computer vision features of the digital content.		 Saravana Balaji Balasubramanian; Jagadeesh Kannan R; Prabu P; Venkatachalam K; Pavel Trojovský; PeerJ. Computer science 2022-07-13
59 Deepfake Video Detection with Spatiotemporal Dropout Transformer
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Highlight: Inspired by that, this paper proposes a simple yet effective patch-level approach to facilitate deepfake video detection via spatiotemporal dropout transformer.		 DAICHI ZHANG et. al. arxiv-cs.CV 2022-07-13
60 Deepfake Face Traceability with Disentangling Reversing Network
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Highlight: Correspondingly, we design a disentangling reversing network that decouples latent space features of deepfake faces under the supervision of fake-original face pair samples to infer original faces in reverse.		 Jiaxin Ai; Zhongyuan Wang; Baojin Huang; Zhen Han; arxiv-cs.CV 2022-07-07
61 Anti-Forgery: Towards A Stealthy and Robust DeepFake Disruption Attack Via Adversarial Perceptual-aware Perturbations
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Highlight: In this paper, we investigate the vulnerability of the existing forgery techniques and propose a novel anti-forgery technique that helps users protect the shared facial images from attackers who are capable of applying the popular forgery techniques.		 RUN WANG et. al. ijcai 2022-07-01
62 DFGC 2022: The Second DeepFake Game Competition
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Highlight: This paper presents the summary report on our DFGC 2022 competition.		 BO PENG et. al. arxiv-cs.CV 2022-06-30
63 Cross-Forgery Analysis of Vision Transformers and CNNs for Deepfake Image Detection
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Highlight: Moreover, continuously retraining a network would be unfeasible. In this paper, we ask ourselves if, among the various deep learning techniques, there is one that is able to generalise the concept of deepfake to such an extent that it does not remain tied to one or more specific deepfake generation methods used in the training set.		 Davide Alessandro Coccomini; Roberto Caldelli; Fabrizio Falchi; Claudio Gennaro; Giuseppe Amato; arxiv-cs.CV 2022-06-28
64 Attack Agnostic Dataset: Towards Generalization and Stabilization of Audio DeepFake Detection
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Highlight: In this work, we introduce Attack Agnostic Dataset – a combination of two audio DeepFakes and one anti-spoofing datasets that, thanks to the disjoint use of attacks, can lead to better generalization of detection methods.		 Piotr Kawa; Marcin Plata; Piotr Syga; arxiv-cs.SD 2022-06-27
65 Learning Brain Representation Using Recurrent Wasserstein Generative Adversarial Net
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Abstract: To understand brain cognition and disorders, modeling the mapping between mind and brain has been of great interest to the neuroscience community. The key is the brain …		 NING QIANG et. al. Computer methods and programs in biomedicine 2022-06-27
66 Protecting President Zelenskyy Against Deep Fakes
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Highlight: This campaign includes at least one example of a deep-fake video purportedly showing Ukrainian President Zelenskyy admitting defeat and surrendering. In anticipation of future attacks of this form, we describe a facial and gestural behavioral model that captures distinctive characteristics of Zelenskyy’s speaking style.		 Matyáš Boháček; Hany Farid; arxiv-cs.CV 2022-06-23
67 Practical Deepfake Detection: Vulnerabilities in Global Contexts
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Highlight: We simulate data corruption techniques and examine the performance of a state-of-the-art deepfake detection algorithm on corrupted variants of the FaceForensics++ dataset.		 Yang A. Chuming; Daniel J. Wu; Ken Hong; arxiv-cs.CV 2022-06-20
68 Deepfake Histological Images for Enhancing Digital Pathology
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Highlight: Here, we present an approach to not only generate histological images that reproduce the diagnostic morphologic features of common disease but also provide a user ability to generate new and rare morphologies.		 KIANOUSH FALAHKHEIRKHAH et. al. arxiv-eess.IV 2022-06-16
69 A Review of Image Processing Techniques for Deepfakes
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Abstract: Deep learning is used to address a wide range of challenging issues including large data analysis, image processing, object detection, and autonomous control. In the same way, …		 HINA FATIMA SHAHZAD et. al. Sensors (Basel, Switzerland) 2022-06-16
70 DeepFake Disrupter: The Detector of DeepFake Is My Friend
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Highlight: In this paper, we propose a novel DeepFake disruption algorithm called "DeepFake Disrupter".		 Xueyu Wang; Jiajun Huang; Siqi Ma; Surya Nepal; Chang Xu; cvpr 2022-06-07
71 Deepfake Caricatures: Amplifying Attention to Artifacts Increases Deepfake Detection By Humans and Machines
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Highlight: It is essential to develop techniques that both detect them, and effectively alert the human user to their presence. Here, we introduce a novel deepfake detection framework that meets both of these needs.		 CAMILO FOSCO et. al. arxiv-cs.CV 2022-06-01
72 Deep Fake Detection Using A Sparse Auto Encoder with A Graph Capsule Dual Graph CNN
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Abstract: Deepfake (DF) is a kind of forged image or video that is developed to spread misinformation and facilitate vulnerabilities to privacy hacking and truth masking with advanced …		 Venkatachalam Kandasamy; Štěpán Hubálovský; Pavel Trojovský; PeerJ. Computer science 2022-05-31
73 A Survey of Deep Fake Detection for Trial Courts
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Highlight: We present extensive discussions and research trends related to DeepFake technologies.		 Naciye Celebi; Qingzhong Liu; Muhammed Karatoprak; arxiv-cs.CV 2022-05-31
74 Misleading Deep-Fake Detection with GAN Fingerprints
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Highlight: These attacks, however, still require certain conditions to hold, such as interacting with the detection method or adjusting the GAN directly. In this paper, we introduce a novel class of simple counterattacks that overcomes these limitations.		 Vera Wesselkamp; Konrad Rieck; Daniel Arp; Erwin Quiring; arxiv-cs.CV 2022-05-25
75 Robust Video Hashing Based on Local Fluctuation Preserving for Tracking Deep Fake Videos
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Highlight: In this paper, a robust video hashing algorithm based on local fluctuation preserving is proposed.		 L. Chen; D. Ye; Y. Shang; J. Huang; icassp 2022-05-22
76 Deepfake Speech Detection Through Emotion Recognition: A Semantic Approach
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Highlight: In this paper, we propose a new audio spoofing detection system leveraging emotional features.		 E. Conti; et al. icassp 2022-05-22
77 ADD 2022: The First Audio Deep Synthesis Detection Challenge
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Highlight: In this paper, we describe the datasets, evaluation metrics, and protocols.		 J. Yi; et al. icassp 2022-05-22
78 Audio Deepfake Detection System with Neural Stitching for ADD 2022
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Highlight: This paper describes our best system and methodology for ADD 2022: The First Audio Deep Synthesis Detection Challenge[1].		 R. YAN et. al. icassp 2022-05-22
79 Custom Attribution Loss for Improving Generalization and Interpretability of Deepfake Detection
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Highlight: In this paper, we approach deepfake detection by solving the related problem of attribution, where the goal is to distinguish each separate type of a deepfake attack.		 P. Korshunov; A. Jain; S. Marcel; icassp 2022-05-22
80 ADT: Anti-Deepfake Transformer
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Highlight: To get out of the predicament of prior methods, in this paper, we propose a novel transformer-based framework to model both global and local information and analyze anomalies of face images.		 P. Wang; et al. icassp 2022-05-22
81 Explaining Deep Learning Models for Spoofing and Deepfake Detection with Shapley Additive Explanations
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Highlight: All results reported in the paper are reproducible using open-source software.		 W. Ge; J. Patino; M. Todisco; N. Evans; icassp 2022-05-22
82 Real-centric Consistency Learning for Deepfake Detection
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Highlight: Essentially, the target of deepfake detection problem is to represent natural faces and fake faces at the representation space discriminatively, and it reminds us whether we could optimize the feature extraction procedure at the representation space through constraining intra-class consistence and inter-class inconsistence to bring the intra-class representations close and push the inter-class representations apart? Therefore, inspired by contrastive representation learning, we tackle the deepfake detection problem through learning the invariant representations of both classes and propose a novel real-centric consistency learning method.		 Ruiqi Zha; Zhichao Lian; Qianmu Li; Siqi Gu; arxiv-cs.CV 2022-05-15
83 The Effectiveness of Temporal Dependency in Deepfake Video Detection
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Highlight: This paper investigates whether temporal information can improve the deepfake detection performance of deep learning models. To investigate this, we propose a framework that classifies new and existing approaches by their defining characteristics.		 Will Rowan; Nick Pears; arxiv-cs.CV 2022-05-13
84 A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials
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Highlight: To simulate the wild scenes, this paper suggests a continual deepfake detection benchmark (CDDB) over a new collection of deepfakes from both known and unknown generative models.		 CHUQIAO LI et. al. arxiv-cs.CV 2022-05-11
85 Countering Malicious DeepFakes: Survey, Battleground, and Horizon
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Abstract: The creation or manipulation of facial appearance through deep generative approaches, known as , have achieved significant progress and promoted a wide range of benign and …		 FELIX JUEFEI-XU et. al. International journal of computer vision 2022-05-04
86 Exposing Deepfake Face Forgeries with Guided Residuals
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Highlight: In this paper, we propose a guided residuals network, namely GRnet, which fuses spatial-domain and residual-domain features in a mutually reinforcing way, to expose face images generated by Deepfake.		 Zhiqing Guo; Gaobo Yang; Jiyou Chen; Xingming Sun; arxiv-cs.CV 2022-05-02
87 DefakeHop++: An Enhanced Lightweight Deepfake Detector
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Highlight: On the basis of DefakeHop, an enhanced lightweight Deepfake detector called DefakeHop++ is proposed in this work.		 Hong-Shuo Chen; Shuowen Hu; Suya You; C. -C. Jay Kuo; arxiv-cs.CV 2022-04-30
88 A Universal Detection Method for Adversarial Examples and Fake Images
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Abstract: Deep-learning technologies have shown impressive performance on many tasks in recent years. However, there are multiple serious security risks when using deep-learning …		 Jiewei Lai; Yantong Huo; Ruitao Hou; Xianmin Wang; Sensors (Basel, Switzerland) 2022-04-30
89 BZNet: Unsupervised Multi-scale Branch Zooming Network for Detecting Low-quality Deepfake Videos
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Highlight: We propose a novel LQ DF detection architecture, multi-scale Branch Zooming Network (BZNet), which adopts an unsupervised super-resolution (SR) technique and utilizes multi-scale images for training.		 Sangyup Lee; Jaeju An; Simon S. Woo; www 2022-04-29
90 Am I A Real or Fake Celebrity? Evaluating Face Recognition and Verification APIs Under Deepfake Impersonation Attack
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Highlight: By conducting a case study on celebrity face recognition, we examine the robustness of black-box commercial face recognition web APIs and open-source tools against Deepfake Impersonation (DI) attacks.		 Shahroz Tariq; Sowon Jeon; Simon S. Woo; www 2022-04-29
91 The MeVer DeepFake Detection Service: Lessons Learnt from Developing and Deploying in The Wild
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Highlight: In this paper, we introduce the MeVer DeepFake detection service, a web service detecting deep learning manipulations in images and video.		 SPYRIDON BAXEVANAKIS et. al. arxiv-cs.CV 2022-04-27
92 Restricted Black-box Adversarial Attack Against DeepFake Face Swapping
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Highlight: Specially, we propose the Transferable Cycle Adversary Generative Adversarial Network (TCA-GAN) to construct the adversarial perturbation for disrupting unknown DeepFake systems.		 Junhao Dong; Yuan Wang; Jianhuang Lai; Xiaohua Xie; arxiv-cs.CV 2022-04-26
93 Audio Deep Fake Detection System with Neural Stitching for ADD 2022
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Highlight: This paper describes our best system and methodology for ADD 2022: The First Audio Deep Synthesis Detection Challenge\cite{Yi2022ADD}.		 RUI YAN et. al. arxiv-eess.AS 2022-04-19
94 Metamorphic Testing-based Adversarial Attack to Fool Deepfake Detectors
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Highlight: We performed our evaluations on MesoInception-4 and TwoStreamNet models, which are the state-of-the-art deepfake detection models.		 Nyee Thoang Lim; Meng Yi Kuan; Muxin Pu; Mei Kuan Lim; Chun Yong Chong; arxiv-cs.CV 2022-04-18
95 Deepfake Video Detection Based on EfficientNet-V2 Network
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Abstract: As technology advances and society evolves, deep learning is becoming easier to operate. Many unscrupulous people are using deep learning technology to create fake pictures and …		 Liwei Deng; Hongfei Suo; Dongjie Li; Computational intelligence and neuroscience 2022-04-15
96 Do You Really Mean That? Content Driven Audio-Visual Deepfake Dataset and Multimodal Method for Temporal Forgery Localization
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Highlight: However, a sophisticated deepfake may contain only a small segment of video/audio manipulation, through which the meaning of the content can be, for example, completely inverted from sentiment perspective. To address this gap, we introduce a content driven audio-visual deepfake dataset, termed as Localized Audio Visual DeepFake (LAV-DF), explicitly designed for the task of learning temporal forgery localization.		 Zhixi Cai; Kalin Stefanov; Abhinav Dhall; Munawar Hayat; arxiv-cs.CV 2022-04-13
97 Fake News Detection Using Parallel BERT Deep Neural Networks
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Highlight: This article, we introduce MWPBert, which uses two parallel BERT networks to perform veracity detection on full-text news articles.		 Mahmood Farokhian; Vahid Rafe; Hadi Veisi; arxiv-cs.CL 2022-04-10
98 On The Exploitation of Deepfake Model Recognition
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Highlight: In this work, a robust processing pipeline to evaluate the possibility to point-out analytic fingerprints for Deepfake model recognition is presented.		 Luca Guarnera; Oliver Giudice; Matthias Niessner; Sebastiano Battiato; arxiv-cs.CV 2022-04-09
99 On Improving Cross-dataset Generalization of Deepfake Detectors
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Highlight: In this paper, we formulate deep fake detection as a hybrid combination of supervised and reinforcement learning (RL) to improve its cross-dataset generalization performance.		 Aakash Varma Nadimpalli; Ajita Rattani; arxiv-cs.CV 2022-04-08
100 Facial Recognition Law in China
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Abstract: Although the prevalence of facial recognition-based COVID-19 surveillance tools and techniques, China does not have a facial recognition law to protect its residents’ facial data. …		 ZHAOHUI SU et. al. Journal of medical ethics 2022-04-05
101 FaceSigns: Semi-Fragile Neural Watermarks for Media Authentication and Countering Deepfakes
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Highlight: However, such classifiers do not generalize well to black-box image synthesis techniques and have been shown to be vulnerable to adversarial examples. To address these challenges, we introduce a deep learning based semi-fragile watermarking technique that allows media authentication by verifying an invisible secret message embedded in the image pixels.		 PAARTH NEEKHARA et. al. arxiv-cs.CV 2022-04-04
102 Does Audio Deepfake Detection Generalize?
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Highlight: Which factors contribute to success, and which are accidental? In this work, we address this problem: We systematize audio spoofing detection by re-implementing and uniformly evaluating architectures from related work.		 Nicolas M. Müller; Pavel Czempin; Franziska Dieckmann; Adam Froghyar; Konstantin Böttinger; arxiv-cs.SD 2022-03-30
103 Improved Optical Flow Estimation Method for Deepfake Videos
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Abstract: Creating deepfake multimedia, and especially deepfake videos, has become much easier these days due to the availability of deepfake tools and the virtually unlimited numbers of …		 Ali Bou Nassif; Qassim Nasir; Manar Abu Talib; Omar Mohamed Gouda; Sensors (Basel, Switzerland) 2022-03-24
104 A Novel Framework for Assessment of Learning-based Detectors in Realistic Conditions with Application to Deepfake Detection
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Highlight: This paper proposes a rigorous framework to assess performance of learning-based detectors in more realistic situations.		 Yuhang Lu; Ruizhi Luo; Touradj Ebrahimi; arxiv-cs.CV 2022-03-22
105 A New Approach to Improve Learning-based Deepfake Detection in Realistic Conditions
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Highlight: This paper proposes a more effective data augmentation scheme based on real-world image degradation process.		 Yuhang Lu; Touradj Ebrahimi; arxiv-cs.CV 2022-03-22
106 Making DeepFakes More Spurious: Evading Deep Face Forgery Detection Via Trace Removal Attack
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Highlight: In this paper, we solve the above challenges with presenting a novel detector-agnostic trace removal attack for DeepFake anti-forensics.		 Chi Liu; Huajie Chen; Tianqing Zhu; Jun Zhang; Wanlei Zhou; arxiv-cs.CV 2022-03-21
107 Deepfake Style Transfer Mixture: A First Forensic Ballistics Study on Synthetic Images
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Highlight: To this aim, in this paper a first approach to investigate the image ballistics on deepfake images subject to style-transfer manipulations is proposed.		 Luca Guarnera; Oliver Giudice; Sebastiano Battiato; arxiv-cs.CV 2022-03-18
108 Fairness Evaluation in Deepfake Detection Models Using Metamorphic Testing
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Highlight: Therefore, the objective of this work is to adopt metamorphic testing to examine the reliability of the selected deepfake detection model, and how the transformation of input variation places influence on the output.		 Muxin Pu; Meng Yi Kuan; Nyee Thoang Lim; Chun Yong Chong; Mei Kuan Lim; arxiv-cs.CV 2022-03-13
109 An Audio-Visual Attention Based Multimodal Network for Fake Talking Face Videos Detection
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Highlight: Inspired by the decision-making mechanism of human multisensory perception system, which enables the auditory information to enhance post-sensory visual evidence for informed decisions output, in this study, a fake talking face detection framework FTFDNet is proposed by incorporating audio and visual representation to achieve more accurate fake talking face videos detection.		 GANGLAI WANG et. al. arxiv-cs.CV 2022-03-10
110 Voice-Face Homogeneity Tells Deepfake
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Highlight: As a result, the development of generalizability of these approaches has reached a blockage. To address this issue, given the empirical results that the identities behind voices and faces are often mismatched in deepfake videos, and the voices and faces have homogeneity to some extent, in this paper, we propose to perform the deepfake detection from an unexplored voice-face matching view.		 HARRY CHENG et. al. arxiv-cs.CV 2022-03-04
111 Towards Benchmarking and Evaluating Deepfake Detection
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Highlight: Our objective is to establish a comprehensive and consistent benchmark, to develop a repeatable evaluation procedure, and to measure the performance of a range of detection approaches so that the results can be compared soundly.		 CHENHAO LIN et. al. arxiv-cs.CV 2022-03-03
112 Self-supervised Transformer for Deepfake Detection
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Highlight: In this paper, we propose a self-supervised transformer based audio-visual contrastive learning method.		 Hanqing Zhao; Wenbo Zhou; Dongdong Chen; Weiming Zhang; Nenghai Yu; arxiv-cs.CV 2022-03-02
113 Compliance Challenges in Forensic Image Analysis Under The Artificial Intelligence Act
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Highlight: In this paper, we review why the use of machine learning in forensic image analysis is classified as high-risk.		 Benedikt Lorch; Nicole Scheler; Christian Riess; arxiv-cs.CV 2022-03-01
114 Deepfake Network Architecture Attribution
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Highlight: In this work, we present the first study on Deepfake Network Architecture Attribution to attribute fake images on architecture-level.		 Tianyun Yang; Ziyao Huang; Juan Cao; Lei Li; Xirong Li; arxiv-cs.CV 2022-02-28
115 MRI-GAN: A Generalized Approach to Detect DeepFakes Using Perceptual Image Assessment
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Highlight: In this paper, we describe our work to develop general, deep learning-based models to classify DeepFake content.		 Pratikkumar Prajapati; Chris Pollett; arxiv-cs.CV 2022-02-28
116 Human Detection of Political Deepfakes Across Transcripts, Audio, and Video
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Highlight: We find access to audio and visual communication modalities improve participants’ accuracy.		 Matthew Groh; Aruna Sankaranarayanan; Andrew Lippman; Rosalind Picard; arxiv-cs.HC 2022-02-25
117 Model Attribution of Face-swap Deepfake Videos
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Highlight: In this paper, we fill this gap by studying the model attribution problem of Deepfake videos.		 Shan Jia; Xin Li; Siwei Lyu; arxiv-cs.CV 2022-02-25
118 Deepfake Detection for Facial Images with Facemasks
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Highlight: In this paper, we thoroughly evaluate the performance ofstate-of-the-art deepfake detection models on the deepfakes withthe facemask.		 DONGGEUN KO et. al. arxiv-cs.CV 2022-02-23
119 Deepfake Video Detection Using Biologically Inspired Geometric Deep Learning
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Highlight: The proposed model is trained on publicly available databases of manipulated and real videos, including FaceForensics, FaceForensics++, and Deep Fakes Dataset, with more than 500 gigabytes of data available.		 STEVEN LUO et. al. aps 2022-02-22
120 Seeing Is Living? Rethinking The Security of Facial Liveness Verification in The Deepfake Era
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Highlight: Specifically, we present LiveBugger, a new deepfake-powered attack framework that enables customizable, automated security evaluation of FLV.		 CHANGJIANG LI et. al. arxiv-cs.CR 2022-02-22
121 Deep Learning Model for Deep Fake Face Recognition and Detection
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Abstract: Deep Learning is an effective technique and used in various fields of natural language processing, computer vision, image processing and machine vision. Deep fakes uses deep …		 SUGANTHI ST et. al. PeerJ. Computer science 2022-02-22
122 ADD 2022: The First Audio Deep Synthesis Detection Challenge
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Highlight: In this paper, we describe the datasets, evaluation metrics, and protocols.		 JIANGYAN YI et. al. arxiv-cs.SD 2022-02-16
123 Robust Deepfake On Unrestricted Media: Generation And Detection
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Highlight: This chapter explores the evolution of and challenges in deepfake generation and detection.		 Trung-Nghia Le; Huy H Nguyen; Junichi Yamagishi; Isao Echizen; arxiv-cs.CV 2022-02-13
124 A Review of Deep Learning-based Approaches for Deepfake Content Detection
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Highlight: This paper presents a comprehensive review of recent studies for deepfake content detection using deep learning-based approaches.		 LEANDRO A. PASSOS et. al. arxiv-cs.CV 2022-02-12
125 Towards Adversarially Robust Deepfake Detection: An Ensemble Approach
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Highlight: We propose Disjoint Deepfake Detection (D3), a deepfake detector designed to improve adversarial robustness beyond de facto solutions such as adversarial training.		 ASHISH HOODA et. al. arxiv-cs.LG 2022-02-11
126 Responsible Disclosure of Generative Models Using Scalable Fingerprinting
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Highlight: Our work enables a responsible disclosure of generative models, that allows model inventors to fingerprint their models, so that the generated samples containing a fingerprint can be accurately detected and attributed to a source.		 Ning Yu; Vladislav Skripniuk; Dingfan Chen; Larry S. Davis; Mario Fritz; iclr 2022-02-08
127 FrePGAN: Robust Deepfake Detection Using Frequency-level Perturbations
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Highlight: Thus, we design a framework to generalize the deepfake detector for both the known and unseen GAN models.		 Yonghyun Jeong; Doyeon Kim; Youngmin Ro; Jongwon Choi; arxiv-cs.CV 2022-02-07
128 A New Face Swap Method for Image and Video Domains: A Technical Report
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Highlight: We introduce a new face swap pipeline that is based on FaceShifter architecture and fixes the problems stated above.		 Daniil Chesakov; Anastasia Maltseva; Alexander Groshev; Andrey Kuznetsov; Denis Dimitrov; arxiv-cs.CV 2022-02-07
129 Delving Into The Local: Dynamic Inconsistency Learning for DeepFake Video Detection
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Highlight: However, these works impose supervisions on sparsely sampled video frames but overlook the local mo- tions among adjacent frames, which instead encode rich in- consistency information that can serve as an efficient indica- tor for DeepFake video detection. To mitigate this issue, we delves into the local motion and propose a novel sampling unit named snippet which contains a few successive videos frames for local temporal inconsistency learning.		 ZHIHAO GU et. al. aaai 2022-02-07
130 CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes
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Highlight: Despite achieving impressive results, these adversarial watermarks have low image-level and model-level transferability, meaning that they can protect only one facial image from one specific deepfake model. To address these issues, we propose a novel solution that can generate a Cross-Model Universal Adversarial Watermark (CMUA-Watermark), protecting a large number of facial images from multiple deepfake models.		 HAO HUANG et. al. aaai 2022-02-07
131 FInfer: Frame Inference-Based Deepfake Detection for High-Visual-Quality Videos
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Highlight: In this paper, we propose a frame inference-based detection framework (FInfer) to solve the problem of high-visual-quality Deepfake detection.		 Juan Hu; Xin Liao; Jinwen Liang; Wenbo Zhou; Zheng Qin; aaai 2022-02-07
132 ADD: Frequency Attention and Multi-View Based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images
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Highlight: In this work, we apply frequency domain learning and optimal transport theory in knowledge distillation (KD) to specifically improve the detection of low-quality compressed deepfake images.		 Le Minh Binh; Simon Woo; aaai 2022-02-07
133 Deepfake Pornography As A Male Gaze on Fan Culture
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Highlight: This essay shows the impact of deepfake technology on fan culture.		 Inna Suvorova; arxiv-cs.CY 2022-02-01
134 Detection of Fake Faces in Videos
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Highlight: In this paper, available video datasets are identified and a pretrained model BlazeFace is used to detect faces, and a ResNet and Xception ensembled architectured neural network trained on the dataset to achieve the goal of detection of fake faces in videos.		 M. Shamanth; Russel Mathias; Dr Vijayalakshmi MN; arxiv-cs.CV 2022-01-28
135 Deepfake Forensics Analysis: An Explainable Hierarchical Ensemble of Weakly Supervised Models
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Abstract: Deepfakes have become exponentially more common and sophisticated in recent years, so much so that forensic specialists, policy makers, and the public alike are anxious about …		 SAMUEL HENRIQUE SILVA et. al. Forensic science international. Synergy 2022-01-27
136 Fake or Real News About COVID-19? Pretrained Transformer Model to Detect Potential Misleading News
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Abstract: The World Health Organization declared the novel coronavirus disease 2019 a pandemic on March 11, 2020. Along with the coronavirus pandemic, a new crisis has emerged, …		 SreeJagadeesh Malla; P J A Alphonse; The European physical journal. Special topics 2022-01-13
137 DeepFake Video Detection Through Facial Sparse Optical Flow Based Light CNN
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Abstract: DeepFake detection has become an attractive research topic with tremendous growth of interests recently. However, existing DeepFake detection studies spare no effort to improve …		 Shuya Fang; Shucheng Wang; Rongjun Ye; Journal of Physics: Conference Series 2022-01-01
138 More Real Than Real: A Study on Human Visual Perception of Synthetic Faces [Applications Corner]
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Abstract: Deep fakes have recently become popular. The term refers to doctored media content where one’s face is swapped with someone else’s face or performs someone else’s face movements. …		 FEDERICA LAGO et. al. IEEE Signal Processing Magazine 2022-01-01
139 The Protection of Megascience Projects from Deepfake Technologies Threats: Information Law Aspects
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Abstract: The paper examines the potential threats of the malicious use of deepfake technology to destabilize and discredit megascience projects in the global information space. The …		 E I Galyashina; V D Nikishin; Journal of Physics: Conference Series 2022-01-01
140 A Landscape View of Deepfake Techniques and Detection Methods
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Abstract: Deep fakes is the process of changing the information of the image or video with different techniquesand methods that start with humor and fun and sometimes reach economic, …		 Ahmed S Abdulreda; Ahmed J. Obaid; International Journal of Nonlinear Analysis and Applications 2022-01-01
141 Persian Fake News Detection: Neural Representation and Classification at Word and Text Levels
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Abstract: Nowadays, broadcasting news on social media and websites has grown at a swifter pace, which has had negative impacts on both the general public and governments; hence, this has …		 Mohammadreza Samadi; Maryam Mousavian; Saeedeh Momtazi; ACM Transactions on Asian and Low-Resource Language … 2022-01-01
142 Enriching Conventional Ensemble Learner with Deep Contextual Semantics to Detect Fake News in Urdu
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Abstract: Increased connectivity has contributed greatly in facilitating rapid access to information and reliable communication. However, the uncontrolled information dissemination has also …		 Ramsha Saeed; Hammad Afzal; Haider Abbas; Maheen Fatima; ACM Transactions on Asian and Low-Resource Language … 2022-01-01
143 Computational Analysis of Transition Temperatures (Tts) of Proteins Fused to Elastin-like Polypeptide (ELP): Deep Fake Evaluation of Proteins, Linkers, and Trailers Features
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Highlight: In this paper, two unique quantitative models are presented for the prediction of the Tt of a family of ELPs that could be fused to different proteins, linkers, and trailers.		 M. D. GHAFARI et. al. bio.bioinformatics 2021-12-30
144 DA-FDFtNet: Dual Attention Fake Detection Fine-tuning Network to Detect Various AI-Generated Fake Images
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Highlight: In this work, we propose Dual Attention Fake Detection Fine-tuning Network (DA-FDFtNet) to detect the manipulated fake face images from the real face data.		 Young Oh Bang; Simon S. Woo; arxiv-cs.CV 2021-12-22
145 MFF-Net: Deepfake Detection Network Based on Multi-Feature Fusion
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Abstract: Significant progress has been made in generating counterfeit images and videos. Forged videos generated by deepfaking have been widely spread and have caused severe societal …		 Lei Zhao; Mingcheng Zhang; Hongwei Ding; Xiaohui Cui; Entropy (Basel, Switzerland) 2021-12-17
146 Comparative Analysis of Deepfake Image Detection Method Using Convolutional Neural Network
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Abstract: Generation Z is a data-driven generation. Everyone has the entirety of humanity’s knowledge in their hands. The technological possibilities are endless. However, we use and misuse …		 HASIN SHAHED SHAD et. al. Computational intelligence and neuroscience 2021-12-16
147 Vision Transformer Based Video Hashing Retrieval for Tracing The Source of Fake Videos
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Highlight: We use an improved retrieval method to find the original video, named ViTHash.		 Pengfei Pei; Xianfeng Zhao; Yun Cao; Jinchuan Li; Xuyuan Lai; arxiv-cs.CV 2021-12-15
148 Physics Guided Deep Learning Generative Models for Crystal Materials Discovery
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Highlight: Naively transferred generative models tend to generate a large portion of physically infeasible crystal structures that are not stable or synthesizable.		 Yong Zhao; Edirisuriya MD Siriwardane; Jianjun Hu; arxiv-cond-mat.mtrl-sci 2021-12-07
149 ADD: Frequency Attention and Multi-View Based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images
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Highlight: In this work, we apply frequency domain learning and optimal transport theory in knowledge distillation (KD) to specifically improve the detection of low-quality compressed deepfake images.		 Binh M. Le; Simon S. Woo; arxiv-cs.CV 2021-12-07
150 Audio Deepfake Perceptions in College Going Populations
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Highlight: In this research we focus on audio deepfakes and how people perceive it.		 Gabrielle Watson; Zahra Khanjani; Vandana P. Janeja; arxiv-cs.SD 2021-12-06
151 How Deep Are The Fakes? Focusing on Audio Deepfake: A Survey
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Highlight: We found that Generative Adversarial Networks(GAN), Convolutional Neural Networks (CNN), and Deep Neural Networks (DNN) are common ways of creating and detecting deepfakes.		 Zahra Khanjani; Gabrielle Watson; Vandana P. Janeja; arxiv-cs.SD 2021-11-28
152 A War Beyond Deepfake: Benchmarking Facial Counterfeits and Countermeasures
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Highlight: In this paper, we present a benchmark that provides in-depth insights into visual forgery and visual forensics, using a comprehensive and empirical approach.		 MINH TAM PHAM et. al. arxiv-cs.CV 2021-11-25
153 Deepfake Detection By Human Crowds, Machines, and Machine-informed Crowds
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Abstract: The recent emergence of machine-manipulated media raises an important societal question: How can we know whether a video that we watch is real or fake? In two online studies with …		 Matthew Groh; Ziv Epstein; Chaz Firestone; Rosalind Picard; Proceedings of the National Academy of Sciences of the … 2021-11-25
154 Is Seeing Still Believing? Leveraging Deepfake Technology for Livestock Farming
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Abstract: Deepfake technologies are known for the creation of forged celebrity pornography, face and voice swaps, and other fake media content. Despite the negative connotations the …		 Suresh Neethirajan; Frontiers in veterinary science 2021-11-23
155 The Shallow of Your Smile: The Ethics of Expressive Vocal Deep-fakes
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Abstract: Rapid technological advances in artificial intelligence are creating opportunities for real-time algorithmic modulations of a person’s facial and vocal expressions, or …		 Nadia Guerouaou; Guillaume Vaiva; Jean-Julien Aucouturier; Philosophical transactions of the Royal Society of London. … 2021-11-15
156 An Optimized Hybrid Deep Learning Model to Detect COVID-19 Misleading Information
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Abstract: Fake news is challenging to detect due to mixing accurate and inaccurate information from reliable and unreliable sources. Social media is a data source that is not trustworthy …		 Bader Alouffi; Abdullah Alharbi; Radhya Sahal; Hager Saleh; Computational intelligence and neuroscience 2021-11-15
157 Impact of Benign Modifications on Discriminative Performance of Deepfake Detectors
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Highlight: This paper proposes a more rigorous and systematic framework to assess the performance of deepfake detectors in more realistic situations.		 Yuhang Lu; Evgeniy Upenik; Touradj Ebrahimi; arxiv-cs.CV 2021-11-14
158 DeepFake Electrocardiograms Using Generative Adversarial Networks Are The Beginning of The End for Privacy Issues in Medicine
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Abstract: Recent global developments underscore the prominent role big data have in modern medical science. But privacy issues constitute a prevalent problem for collecting and sharing data …		 VAJIRA THAMBAWITA et. al. Scientific reports 2021-11-09
159 NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Media
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Highlight: We introduce several strategies for automatically retrieving convincing images for a given caption, capturing cases with inconsistent entities or semantic context.		 Grace Luo; Trevor Darrell; Anna Rohrbach; emnlp 2021-11-05
160 Deepfake Detection Using The Rate of Change Between Frames Based on Computer Vision
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Abstract: Recently, artificial intelligence has been successfully used in fields, such as computer vision, voice, and big data analysis. However, various problems, such as security, …		 Gihun Lee; Mihui Kim; Sensors (Basel, Switzerland) 2021-11-05
161 WaveFake: A Data Set to Facilitate Audio Deepfake Detection
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Highlight: In this paper we make three key contributions to narrow this gap. Second, we present a novel data set, for which we collected nine sample sets from five different network architectures, spanning two languages.		 Joel Frank; Lea Schönherr; arxiv-cs.LG 2021-11-04
162 Exposing Deepfake with Pixel-wise AR and PPG Correlation from Faint Signals
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Highlight: In this paper, we propose a scheme to expose Deepfake through faint signals hidden in face videos.		 Maoyu Mao; Jun Yang; arxiv-cs.CV 2021-10-29
163 Deep Learning Algorithms for Magnetic Resonance Imaging of Inflammatory Sacroiliitis in Axial Spondyloarthritis
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Abstract: The aim of this study was to develop a deep learning algorithm for detection of active inflammatory sacroiliitis in short tau inversion recovery (STIR) sequence MRI. A total of …		 Karina Ying Ying Lin; Cao Peng; Kam Ho Lee; Shirley Chiu Wai Chan; Ho Yin Chung; Rheumatology (Oxford, England) 2021-10-22
164 Deepfake Tweets Classification Using Stacked Bi-LSTM and Words Embedding
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Abstract: The spread of altered media in the form of fake videos, audios, and images, has been largely increased over the past few years. Advanced digital manipulation tools and techniques …		 VAIBHAV RUPAPARA et. al. PeerJ. Computer science 2021-10-21
165 PROVES: Establishing Image Provenance Using Semantic Signatures
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Highlight: We propose a novel architecture for preserving the provenance of semantic information in images to make them less susceptible to deep fake attacks.		 MINGYANG XIE et. al. arxiv-cs.CV 2021-10-21
166 A Study On Data Augmentation In Voice Anti-Spoofing
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Highlight: In this paper, we perform an in-depth study of how data augmentation techniques improve synthetic or spoofed audio detection.		 Ariel Cohen; Inbal Rimon; Eran Aflalo; Haim Permuter; arxiv-cs.SD 2021-10-20
167 Fake News Detection in Spanish Using Deep Learning Techniques
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Highlight: This paper addresses the problem of fake news detection in Spanish using Machine Learning techniques.		 Kevin Martínez-Gallego; Andrés M. Álvarez-Ortiz; Julián D. Arias-Londoño; arxiv-cs.CL 2021-10-12
168 Joint Audio-Visual Deepfake Detection
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Highlight: In this work, we propose a novel visual / auditory deepfake joint detection task and show that exploiting the intrinsic synchronization between the visual and auditory modalities could benefit deepfake detection.		 Yipin Zhou; Ser-Nam Lim; iccv 2021-10-08
169 Learning Self-Consistency for Deepfake Detection
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Highlight: We propose a new method to detect deepfake images using the cue of the source feature inconsistency within the forged images.		 TIANCHEN ZHAO et. al. iccv 2021-10-08
170 KoDF: A Large-Scale Korean DeepFake Detection Dataset
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Highlight: In this paper, we provide a detailed description of methods used to construct the dataset, experimentally show the discrepancy between the distributions of KoDF and existing deepfake detection datasets, and underline the importance of using multiple datasets for real-world generalization.		 Patrick Kwon; Jaeseong You; Gyuhyeon Nam; Sungwoo Park; Gyeongsu Chae; iccv 2021-10-08
171 ID-Reveal: Identity-Aware DeepFake Video Detection
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Highlight: To this end, we introduce ID-Reveal, a new approach that learns temporal facial features, specific of how a person moves while talking, by means of metric learning coupled with an adversarial training strategy.		 Davide Cozzolino; Andreas Rossler; Justus Thies; Matthias Niessner; Luisa Verdoliva; iccv 2021-10-08
172 Explaining Deep Learning Models for Spoofing and Deepfake Detection with SHapley Additive ExPlanations
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Highlight: All results reported in the paper are reproducible using open-source software.		 Wanying Ge; Jose Patino; Massimiliano Todisco; Nicholas Evans; arxiv-eess.AS 2021-10-07
173 An Experimental Evaluation on Deepfake Detection Using Deep Face Recognition
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Highlight: In this paper, we thoroughly evaluate the efficacy of deep face recognition in identifying deepfakes, using different loss functions and deepfake generation techniques.		 Sreeraj Ramachandran; Aakash Varma Nadimpalli; Ajita Rattani; arxiv-cs.CV 2021-10-04
174 Daily Briefing: A Chilling ‘deepfake’ History of The Moon Landing
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 Flora Graham; Nature 2021-10-01
175 Improved Xception with Dual Attention Mechanism and Feature Fusion for Face Forgery Detection
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Highlight: In this paper, we propose an improved Xception with dual attention mechanism and feature fusion for face forgery detection.		 Hao Lin; Weiqi Luo; Kangkang Wei; Minglin Liu; arxiv-cs.CV 2021-09-28
176 Machine Learning Based Medical Image Deepfake Detection: A Comparative Study
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Highlight: Specifically, we evaluate eight different machine learning algorithms, which including three conventional machine learning methods, support vector machine, random forest, decision tree, and five deep learning models, DenseNet121, DenseNet201, ResNet50, ResNet101, VGG19, on distinguishing between tampered and untampered images.For deep learning models, the five models are used for feature extraction, then fine-tune for each pre-trained model is performed.		 Siddharth Solaiyappan; Yuxin Wen; arxiv-cs.CV 2021-09-27
177 Deepfake Video Detection: YOLO-Face Convolution Recurrent Approach
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Abstract: Recently, the deepfake techniques for swapping faces have been spreading, allowing easy creation of hyper-realistic fake videos. Detecting the authenticity of a video has become …		 Aya Ismail; Marwa Elpeltagy; Mervat Zaki; Kamal A ElDahshan; PeerJ. Computer science 2021-09-21
178 MD-CSDNetwork: Multi-Domain Cross Stitched Network for Deepfake Detection
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Highlight: Inspired by this observation, in this paper, we present a novel approach, termed as MD-CSDNetwork, for combining the features in the spatial and frequency domains to mine a shared discriminative representation for classifying \textit{deepfakes}.		 Aayushi Agarwal; Akshay Agarwal; Sayan Sinha; Mayank Vatsa; Richa Singh; arxiv-cs.CV 2021-09-15
179 FaceGuard: Proactive Deepfake Detection
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Highlight: In this work, we propose FaceGuard, a proactive deepfake-detection framework.		 Yuankun Yang; Chenyue Liang; Hongyu He; Xiaoyu Cao; Neil Zhenqiang Gong; arxiv-cs.CV 2021-09-12
180 Challenges and Solutions in DeepFakes
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Highlight: So, to counter this emerging problem, we introduce a dataset of 140k real and fake faces which contain 70k real faces from the Flickr dataset collected by Nvidia, as well as 70k fake faces sampled from 1 million fake faces generated by style GAN.		 Jatin Sharma; Sahil Sharma; arxiv-cs.CV 2021-09-11
181 Evaluation of An Audio-Video Multimodal Deepfake Dataset Using Unimodal and Multimodal Detectors
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Highlight: With the emerging threat of potential harm deepfakes can cause, researchers have proposed deepfake detection methods.		 Hasam Khalid; Minha Kim; Shahroz Tariq; Simon S. Woo; arxiv-cs.CV 2021-09-07
182 DeepFakes: Detecting Forged and Synthetic Media Content Using Machine Learning
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Highlight: This study presents challenges, research trends, and directions related to DeepFake creation and detection techniques by reviewing the notable research in the DeepFake domain to facilitate the development of more robust approaches that could deal with the more advance DeepFake in the future.		 SM ZOBAED et. al. arxiv-cs.CV 2021-09-07
183 Spatiotemporal Inconsistency Learning for DeepFake Video Detection
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Highlight: Specifically, we present a novel temporal modeling paradigm in TIM by exploiting the temporal difference over adjacent frames along with both horizontal and vertical directions.		 ZHIHAO GU et. al. arxiv-cs.CV 2021-09-04
184 DeepFake Detection with Inconsistent Head Poses: Reproducibility and Analysis
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Highlight: In this paper, we analyze an existing DeepFake detection technique based on head pose estimation, which can be applied when fake images are generated with an autoencoder-based face swap.		 Kevin Lutz; Robert Bassett; arxiv-cs.CV 2021-08-28
185 DeepFake MNIST+: A DeepFake Facial Animation Dataset
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Highlight: Various DeepFake detection methods and datasets are proposed for detecting such data, especially for face-swapping. As a response, we propose a new human face animation dataset, called DeepFake MNIST+, generated by a SOTA image animation generator.		 Jiajun Huang; Xueyu Wang; Bo Du; Pei Du; Chang Xu; arxiv-cs.CV 2021-08-17
186 Deepfake Representation with Multilinear Regression
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Highlight: In this paper, we propose a modified multilinear (tensor) method, a combination of linear and multilinear regressions for representing fake and real data.		 Sara Abdali; M. Alex O. Vasilescu; Evangelos E. Papalexakis; arxiv-cs.CV 2021-08-15
187 Beyond The Spectrum: Detecting Deepfakes Via Re-Synthesis
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Highlight: In order to overcome this issue, we propose a novel fake detection that is designed to re-synthesize testing images and extract visual cues for detection.		 Yang He; Ning Yu; Margret Keuper; Mario Fritz; ijcai 2021-08-13
188 An Examination of Fairness of AI Models for Deepfake Detection
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Highlight: In this work, we evaluate bias present in deepfake datasets and detection models across protected subgroups.		 Loc Trinh; Yan Liu; ijcai 2021-08-13
189 Dynamic Inconsistency-aware DeepFake Video Detection
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Highlight: In this paper, we propose a novel Dynamic Inconsistency-aware Network to handle the inconsistent problem, which uses a Cross-Reference module (CRM) to capture both the global and local inter-frame inconsistencies.		 ZIHENG HU et. al. ijcai 2021-08-13
190 Detecting Deepfake Videos with Temporal Dropout 3DCNN
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Highlight: To address that, this paper aims to leverage the possible inconsistent cues among video frames and proposes a Temporal Dropout 3-Dimensional Convolutional Neural Network (TD-3DCNN) to detect deepfake videos.		 Daichi Zhang; Chenyu Li; Fanzhao Lin; Dan Zeng; Shiming Ge; ijcai 2021-08-13
191 Video Transformer for Deepfake Detection with Incremental Learning
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Highlight: In this paper, we propose a novel video transformer with incremental learning for detecting deepfake videos.		 Sohail A. Khan; Hang Dai; arxiv-cs.CV 2021-08-11
192 FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset
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Highlight: To fill this gap, we propose a novel Audio-Video Deepfake dataset, FakeAVCeleb, which contains not only deepfake videos but also respective synthesized lip-synced fake audios.		 Hasam Khalid; Shahroz Tariq; Minha Kim; Simon S. Woo; arxiv-cs.CV 2021-08-11
193 A New Deep Learning-Based Methodology for Video Deepfake Detection Using XGBoost
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Abstract: Currently, face-swapping deepfake techniques are widely spread, generating a significant number of highly realistic fake videos that threaten the privacy of people and countries. …		 Aya Ismail; Marwa Elpeltagy; Mervat S Zaki; Kamal Eldahshan; Sensors (Basel, Switzerland) 2021-08-10
194 Using Deep Learning Techniques and Inferential Speech Statistics for AI Synthesised Speech Recognition
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Highlight: Here, we propose a model based on Convolutional Neural Network (CNN) and Bidirectional Recurrent Neural Network (BiRNN) that helps to achieve both the aforementioned objectives.		 Arun Kumar Singh; Priyanka Singh; Karan Nathwani; arxiv-cs.LG 2021-07-23
195 Human Perception of Audio Deepfakes
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Highlight: In this paper, we present results from comparing the abilities of humans and machines for detecting audio deepfakes used to imitate someone’s voice.		 Nicolas M. Müller; Karla Markert; Jennifer Williams; arxiv-cs.HC 2021-07-20
196 CoReD: Generalizing Fake Media Detection with Continual Representation Using Distillation
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Highlight: Therefore, in this work, we apply continuous learning to neural networks’ learning dynamics, emphasizing its potential to increase data efficiency significantly.		 Minha Kim; Shahroz Tariq; Simon S. Woo; arxiv-cs.CV 2021-07-06
197 Combining EfficientNet and Vision Transformers for Video Deepfake Detection
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Highlight: In this study, we focus on video deep fake detection on faces, given that most methods are becoming extremely accurate in the generation of realistic human faces.		 Davide Coccomini; Nicola Messina; Claudio Gennaro; Fabrizio Falchi; arxiv-cs.CV 2021-07-06
198 FFR_FD: Effective and Fast Detection of DeepFakes Based on Feature Point Defects
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Highlight: Inspired by feature point detector-descriptors to extract discriminative features at the pixel level, we propose the Fused Facial Region_Feature Descriptor (FFR_FD) for effective and fast DeepFake detection.		 Gaojian Wang; Qian Jiang; Xin Jin; Xiaohui Cui; arxiv-cs.CV 2021-07-05
199 Understanding The Security of Deepfake Detection
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Highlight: In this work, we aim to bridge the gap.		 Xiaoyu Cao; Neil Zhenqiang Gong; arxiv-cs.CR 2021-07-05
200 Auxiliary-Classifier GAN for Malware Analysis
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Highlight: In this research, we generate fake malware images using auxiliary classifier GANs (AC-GAN), and we consider the effectiveness of various techniques for classifying the resulting images.		 Rakesh Nagaraju; Mark Stamp; arxiv-cs.CR 2021-07-04
201 The Future of Online Trust (and Why Deepfake Is Advancing It)
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Abstract: Trust has become a first-order concept in AI, urging experts to call for measures ensuring AI is ‘trustworthy’. The danger of untrustworthy AI often culminates with Deepfake, …		 Hubert Etienne; AI and ethics 2021-06-28
202 Deepfake Videos in The Wild: Analysis and Detection
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Highlight: To bridge this gap in knowledge, we make the following contributions: First, we collect and present the largest dataset of deepfake videos in the wild, containing 1,869 videos from YouTube and Bilibili, and extract over 4.8M frames of content. Second, we present a comprehensive analysis of the growth patterns, popularity, creators, manipulation strategies, and production methods of deepfake content in the real-world.		 JIAMENG PU et. al. www 2021-06-25
203 One Detector to Rule Them All: Towards A General Deepfake Attack Detection Framework
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Highlight: To better cope with unknown and unseen deepfakes, we introduce a Convolutional LSTM-based Residual Network (CLRNet), which adopts a unique model training strategy and explores spatial as well as the temporal information in a deepfakes.		 Shahroz Tariq; Sangyup Lee; Simon Woo; www 2021-06-25
204 Detection of Deepfake Videos Using Long Distance Attention
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Highlight: In this paper, the problem is treated as a special fine-grained classification problem since the differences between fake and real faces are very subtle.		 WEI LU et. al. arxiv-cs.CV 2021-06-24
205 Deep Fake Detection: Survey of Facial Manipulation Detection Solutions
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Highlight: In this paper, we analyze several such states of the art neural networks (MesoNet, ResNet-50, VGG-19, and Xception Net) and compare them against each other, to find an optimal solution for various scenarios like real-time deep fake detection to be deployed in online social media platforms where the classification should be made as fast as possible or for a small news agency where the classification need not be in real-time but requires utmost accuracy.		 Samay Pashine; Sagar Mandiya; Praveen Gupta; Rashid Sheikh; arxiv-cs.CV 2021-06-23
206 Automated Deepfake Detection
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Highlight: In this paper, we propose to utilize Automated Machine Learning to adaptively search a neural architecture for deepfake detection.		 PING LIU et. al. arxiv-cs.CV 2021-06-20
207 Wavelet-Packets for Deepfake Image Analysis and Detection
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Highlight: The wavelet transform conserves spatial information to a degree, which allows us to present a new analysis.		 Moritz Wolter; Felix Blanke; Raoul Heese; Jochen Garcke; arxiv-cs.CV 2021-06-17
208 Compound Frechet Inception Distance for Quality Assessment of GAN Created Images
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Highlight: We propose to improve the robustness of the evaluation process by integrating lower-level features to cover a wider array of visual defects.		 Eric J. Nunn; Pejman Khadivi; Shadrokh Samavi; arxiv-cs.CV 2021-06-16
209 Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images
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Highlight: We define a novel problem, model parsing, as estimating GM network architectures and training loss functions by examining their generated images – a task seemingly impossible for human beings. To tackle this problem, we propose a framework with two components: a Fingerprint Estimation Network (FEN), which estimates a GM fingerprint from a generated image by training with four constraints to encourage the fingerprint to have desired properties, and a Parsing Network (PN), which predicts network architecture and loss functions from the estimated fingerprints.		 Vishal Asnani; Xi Yin; Tal Hassner; Xiaoming Liu; arxiv-cs.CV 2021-06-15
210 Multi-Attentional Deepfake Detection
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Highlight: In this paper, we instead formulate deepfake detection as a fine-grained classification problem and propose a new multi-attentional deepfake detection network.		 HANQING ZHAO et. al. cvpr 2021-06-11
211 Exploring Adversarial Fake Images on Face Manifold
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Highlight: In this paper, instead of adding adversarial noise, we optimally search adversarial points on face manifold to generate anti-forensic fake face images.		 Dongze Li; Wei Wang; Hongxing Fan; Jing Dong; cvpr 2021-06-11
212 Imperceptible Adversarial Examples for Fake Image Detection
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Highlight: In this paper, we propose a novel method to disrupt the fake image detection by determining key pixels to a fake image detector and attacking only the key pixels, which results in the $L_0$ and the $L_2$ norms of adversarial perturbations much less than those of existing works.		 QUANYU LIAO et. al. arxiv-cs.CV 2021-06-03
213 DFGC 2021: A DeepFake Game Competition
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Highlight: This paper presents a summary of the DFGC 2021 competition. We also release the DFGC-21 testing dataset collected from our participants to further benefit the research community.		 BO PENG et. al. arxiv-cs.CV 2021-06-02
214 A Novel Self-learning Semi-supervised Deep Learning Network to Detect Fake News on Social Media
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Abstract: Social media has become a popular means for people to consume and share news. However, it also enables the extensive spread of fake news, that is, news that deliberately provides …		 Xin Li; Peixin Lu; Lianting Hu; XiaoGuang Wang; Long Lu; Multimedia tools and applications 2021-06-02
215 FReTAL: Generalizing Deepfake Detection Using Knowledge Distillation and Representation Learning
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Highlight: In this work, we employ the Representation Learning (ReL) and Knowledge Distillation (KD) paradigms to introduce a transfer learning-based Feature Representation Transfer Adaptation Learning (FReTAL) method.		 Minha Kim; Shahroz Tariq; Simon S. Woo; arxiv-cs.CV 2021-05-28
216 Subjective and Objective Evaluation of Deepfake Videos
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Highlight: Therefore, this paper, presents a subjective study, which, using 60 naïve subjects, evaluates how hard it is for humans to see if a video is a deepfake or not.		 P. Korshunov; S. Marcel; icassp 2021-05-16
217 TweepFake: About Detecting Deepfake Tweets
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Abstract: The recent advances in language modeling significantly improved the generative capabilities of deep neural models: in 2019 OpenAI released GPT-2, a pre-trained language model that …		 Tiziano Fagni; Fabrizio Falchi; Margherita Gambini; Antonio Martella; Maurizio Tesconi; PloS one 2021-05-13
218 TAR: Generalized Forensic Framework to Detect Deepfakes Using Weakly Supervised Learning
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Highlight: Therefore, in this work, we introduce a practical digital forensic tool to detect different types of deepfakes simultaneously and propose Transfer learning-based Autoencoder with Residuals (TAR).		 Sangyup Lee; Shahroz Tariq; Junyaup Kim; Simon S. Woo; arxiv-cs.CV 2021-05-13
219 What’s Wrong with This Video? Comparing Explainers for Deepfake Detection
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Highlight: In this work we develop, extend and compare white-box, black-box and model-specific techniques for explaining the labelling of real and fake videos.		 Samuele Pino; Mark James Carman; Paolo Bestagini; arxiv-cs.CV 2021-05-12
220 DeepFake Electrocardiograms: The Key for Open Science for Artificial Intelligence in Medicine
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Highlight: Here, we present generative adversarial networks (GANs) capable of generating realistic synthetic DeepFake 12-lead 10-sec electrocardiograms (ECGs).		 V. L. THAMBAWITA et. al. med.cardiovascular-medicine 2021-05-10
221 One Detector to Rule Them All: Towards A General Deepfake Attack Detection Framework
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Highlight: To better cope with unknown and unseen deepfakes, we introduce a Convolutional LSTM-based Residual Network (CLRNet), which adopts a unique model training strategy and explores spatial as well as the temporal information in deepfakes.		 Shahroz Tariq; Sangyup Lee; Simon S. Woo; arxiv-cs.CV 2021-05-01
222 Deep Insights of Deepfake Technology : A Review
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Highlight: Under the aegis of computer vision and deep learning technology, a new emerging techniques has introduced that anyone can make highly realistic but fake videos, images even can manipulates the voices.		 Bahar Uddin Mahmud; Afsana Sharmin; arxiv-cs.LG 2021-05-01
223 Deepfake False Memories
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Abstract: Machine-learning has enabled the creation of deepfake videos; highly-realistic footage that features a person saying or doing something they never did. In recent years, this …		 Gillian Murphy; Emma Flynn; Memory (Hove, England) 2021-04-28
224 DeepfakeUCL: Deepfake Detection Via Unsupervised Contrastive Learning
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Highlight: In this paper, we design a novel deepfake detection method via unsupervised contrastive learning.		 Sheldon Fung; Xuequan Lu; Chao Zhang; Chang-Tsun Li; arxiv-cs.CV 2021-04-23
225 Safe Fakes: Evaluating Face Anonymizers for Face Detectors
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Highlight: To our knowledge, this paper presents the first empirical study on the effect of image anonymization on supervised training of face detectors.		 Sander R. Klomp; Matthew van Rijn; Rob G. J. Wijnhoven; Cees G. M. Snoek; Peter H. N. de With; arxiv-cs.CV 2021-04-23
226 M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection
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Highlight: In this paper, we aim to capture the subtle manipulation artifacts at different scales using transformer models.		 JUNKE WANG et. al. arxiv-cs.CV 2021-04-20
227 Deepfake Detection Scheme Based on Vision Transformer and Distillation
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Highlight: In this paper, we propose a Vision Transformer model with distillation methodology for detecting fake videos.		 Young-Jin Heo; Young-Ju Choi; Young-Woon Lee; Byung-Gyu Kim; arxiv-cs.CV 2021-04-03
228 A Comprehensive Review of Deep-Learning-Based Methods for Image Forensics
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Abstract: Seeing is not believing anymore. Different techniques have brought to our fingertips the ability to modify an image. As the difficulty of using such techniques decreases, lowering …		 Ivan Castillo Camacho; Kai Wang; Journal of imaging 2021-04-03
229 The Real Threat of Deepfake Pornography: A Review of Canadian Policy
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Abstract: Deepfakes may refer to algorithmically synthesized material wherein the face of a person is superimposed onto another body. To date, most deepfakes found online are pornographic, …		 Vasileia Karasavva; Aalia Noorbhai; Cyberpsychology, behavior and social networking 2021-03-24
230 The (Null) Effects of Happiness on Affective Polarization, Conspiracy Endorsement, and Deep Fake Recognition: Evidence from Five Survey Experiments in Three Countries
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Abstract: Affective polarization is a key concern in America and other democracies. Although past evidence suggests some ways to minimize it, there are no easily applicable interventions …		 XUDONG YU et. al. Political behavior 2021-03-18
231 DefakeHop: A Light-Weight High-Performance Deepfake Detector
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Highlight: A light-weight high-performance Deepfake detection method, called DefakeHop, is proposed in this work.		 HONG-SHUO CHEN et. al. arxiv-cs.CV 2021-03-11
232 An Introduction to Deep Generative Modeling
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Highlight: Our goal is to enable and motivate the reader to contribute to this proliferating research area.		 Lars Ruthotto; Eldad Haber; arxiv-cs.LG 2021-03-08
233 DeepFake-o-meter: An Open Platform for DeepFake Detection
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Highlight: In this work, we develop an open-source online platform, known as DeepFake-o-meter, that integrates state-of-the-art DeepFake detection methods and provide a convenient interface for the users.		 Yuezun Li; Cong Zhang; Pu Sun; Honggang Qi; Siwei Lyu; arxiv-cs.CV 2021-03-02
234 Am I A Real or Fake Celebrity? Measuring Commercial Face Recognition Web APIs Under Deepfake Impersonation Attack
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Highlight: This work provides a measurement study on the robustness of black-box commercial face recognition APIs against Deepfake Impersonation (DI) attacks using celebrity recognition APIs as an example case study.		 Shahroz Tariq; Sowon Jeon; Simon S. Woo; arxiv-cs.CV 2021-03-01
235 Effects of Disinformation Using Deepfake: The Protective Effect of Media Literacy Education
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Abstract: This research examines (a) the negative impact of disinformation including a deepfake video and (b) the protective effect of media literacy education. We conducted an experiment …		 Yoori Hwang; Ji Youn Ryu; Se-Hoon Jeong; Cyberpsychology, behavior and social networking 2021-03-01
236 Deepfakes Unmasked: The Effects of Information Priming and Bullshit Receptivity on Deepfake Recognition and Sharing Intention
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Abstract: The study aims to test whether simple priming of deepfake (DF) information significantly increases users’ ability to recognize DF media. Although undoubtedly fascinating from a …		 Serena Iacobucci; Roberta De Cicco; Francesca Michetti; Riccardo Palumbo; Stefano Pagliaro; Cyberpsychology, behavior and social networking 2021-02-26
237 Deepfakes Generation and Detection: State-of-the-art, Open Challenges, Countermeasures, and Way Forward
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Highlight: This paper provides a comprehensive review and detailed analysis of existing tools and machine learning (ML) based approaches for deepfake generation and the methodologies used to detect such manipulations for both audio and visual deepfakes.		 Momina Masood; Marriam Nawaz; Khalid Mahmood Malik; Ali Javed; Aun Irtaza; arxiv-cs.CR 2021-02-25
238 Deepfake Video Detection Using Convolutional Vision Transformer
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Highlight: In this work, we propose a Convolutional Vision Transformer for the detection of Deepfakes.		 Deressa Wodajo; Solomon Atnafu; arxiv-cs.CV 2021-02-22
239 Towards Solving The DeepFake Problem : An Analysis on Improving DeepFake Detection Using Dynamic Face Augmentation
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Highlight: In this paper, we focus on identifying the limitations and shortcomings of existing deepfake detection frameworks. Moreover, we also propose a general-purpose data pre-processing guideline to train and evaluate existing architectures allowing us to improve the generalizability of these models for deepfake detection.		 Sowmen Das; Selim Seferbekov; Arup Datta; Md. Saiful Islam; Md. Ruhul Amin; arxiv-cs.CV 2021-02-18
240 Popular Discourse Around Deepfakes and The Interdisciplinary Challenge of Fake Video Distribution
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Abstract: This research interrogates the discourses that frame our understanding of deepfakes and how they are situated in everyday public conversation. It does so through a qualitative …		 Catherine Francis Brooks; Cyberpsychology, behavior and social networking 2021-02-17
241 To Believe or Not to Believe: Framing Analysis of Content and Audience Response of Top 10 Deepfake Videos on YouTube
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Abstract: This study explores popular deepfake media content and audience response in an effort to gain better understanding of the potential social and psychological impacts of deepfakes. …		 YoungAh Lee; Kuo-Ting Tim Huang; Robin Blom; Rebecca Schriner; Carl A Ciccarelli; Cyberpsychology, behavior and social networking 2021-02-17
242 Adversarially Robust Deepfake Media Detection Using Fused Convolutional Neural Network Predictions
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Highlight: To address this, we employ three different deep Convolutional Neural Network (CNN) models, (1) VGG16, (2) InceptionV3, and (3) XceptionNet to classify fake and real images extracted from videos.		 Sohail Ahmed Khan; Alessandro Artusi; Hang Dai; arxiv-cs.CV 2021-02-11
243 Domain General Face Forgery Detection By Learning to Weight
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Highlight: In this paper, we propose a domain-general model, termed learning-to-weight (LTW), that guarantees face detection performance across multiple domains, particularly the target domains that are never seen before.		 KE SUN et. al. aaai 2021-02-09
244 CoAID-DEEP: An Optimized Intelligent Framework for Automated Detecting COVID-19 Misleading Information on Twitter
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Abstract: COVID-19 has affected all peoples’ lives. Though COVID-19 is on the rising, the existence of misinformation about the virus also grows in parallel. Additionally, the spread of …		 DIAA SALAMA ABDELMINAAM et. al. IEEE access : practical innovations, open solutions 2021-02-09
245 Landmark Breaker: Obstructing DeepFake By Disturbing Landmark Extraction
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Highlight: In this paper, we describe Landmark Breaker, the first dedicated method to disrupt facial landmark extraction, and apply it to the obstruction of the generation of DeepFake videos.Our motivation is that disrupting the facial landmark extraction can affect the alignment of input face so as to degrade the DeepFake quality.		 Pu Sun; Yuezun Li; Honggang Qi; Siwei Lyu; arxiv-cs.CV 2021-02-01
246 Detecting Deepfake Videos Using Euler Video Magnification
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Highlight: In this paper, we examine a technique for possible identification of deepfake videos.		 Rashmiranjan Das; Gaurav Negi; Alan F. Smeaton; arxiv-cs.CV 2021-01-27
247 Fighting Deepfakes By Detecting GAN DCT Anomalies
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Highlight: For this reason, in this paper we propose a new pipeline able to detect the so-called GAN Specific Frequencies (GSF) representing a unique fingerprint of the different generative architectures.		 Oliver Giudice; Luca Guarnera; Sebastiano Battiato; arxiv-cs.CV 2021-01-24
248 BERT Transformer Model for Detecting Arabic GPT2 Auto-Generated Tweets
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Highlight: In this paper, we propose a transfer learning based model that will be able to detect if an Arabic sentence is written by humans or automatically generated by bots.		 Fouzi Harrag; Maria Debbah; Kareem Darwish; Ahmed Abdelali; arxiv-cs.CL 2021-01-22
249 FakeBuster: A DeepFakes Detection Tool for Video Conferencing Scenarios
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Highlight: This paper proposes a new DeepFake detector FakeBuster for detecting impostors during video conferencing and manipulated faces on social media.		 Vineet Mehta; Parul Gupta; Ramanathan Subramanian; Abhinav Dhall; arxiv-cs.CV 2021-01-09
250 Data Poisoning Attacks to Deep Learning Based Recommender Systems
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Highlight: In this work, we conduct the first systematic study on data poisoning attacks to deep learning based recommender systems.		 HAI HUANG et. al. arxiv-cs.CR 2021-01-07
251 FakeBERT: Fake News Detection in Social Media with A BERT-based Deep Learning Approach
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Abstract: In the modern era of computing, the news ecosystem has transformed from old traditional print media to social media outlets. Social media platforms allow us to consume news much …		 Rohit Kumar Kaliyar; Anurag Goswami; Pratik Narang; Multimedia tools and applications 2021-01-07
252 WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection
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Highlight: To better support detection against real-world deepfakes, in this paper, we introduce a new dataset WildDeepfake, which consists of 7,314 face sequences extracted from 707 deepfake videos collected completely from the internet.		 Bojia Zi; Minghao Chang; Jingjing Chen; Xingjun Ma; Yu-Gang Jiang; arxiv-cs.CV 2021-01-05
253 Where Do Deep Fakes Look? Synthetic Face Detection Via Gaze Tracking
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Highlight: We evaluate our approach on several deep fake datasets, achieving 92.48% accuracy on FaceForensics++, 80.0% on Deep Fakes (in the wild), 88.35% on CelebDF, and 99.27% on DeeperForensics datasets.		 Ilke Demir; Umur A. Ciftci; arxiv-cs.CV 2021-01-04
254 EchoFakeD: Improving Fake News Detection in Social Media with An Efficient Deep Neural Network
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Abstract: The increasing popularity of social media platforms has simplified the sharing of news articles that have led to the explosion in fake news. With the emergence of fake news at a …		 Rohit Kumar Kaliyar; Anurag Goswami; Pratik Narang; Neural computing & applications 2021-01-02
255 Deepfake Detection Using SVM
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Abstract: In recent years the Deep generative networks have made it easy to create real face swaps in images and videos with less traces of manipulation, significantly improving the quality …		 Harsh Agarwal; Ankur Singh; Rajeswari D; 2021 Second International Conference on Electronics and … 2021-01-01
256 Deepfakes Detection Methods: A Literature Survey
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Abstract: Recently, a great amount of concern has been attracted to the phenomena of DeepFake, which has been created for capturing and reenacting faces in a video and swap a face with …		 MC Weerawardana; TGI Fernando; 2021 10th International Conference on Information and … 2021-01-01
257 Deepfake-Image Anti-Forensics with Adversarial Examples Attacks
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Abstract: Many deepfake-image forensic detectors have been proposed and improved due to the development of synthetic techniques. However, recent studies show that most of these detectors …		 Li Fan; Wei Li; Xiaohui Cui; Future Internet 2021-01-01
258 Deep Fakes
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 Yair Sharan; Ted J. Gordon; Elizabeth Florescu; Tripping Points on the Roads to Outwit Terror 2021-01-01
259 The Creation and Detection of Deepfakes
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Abstract: Generative deep learning algorithms have progressed to a point where it is difficult to tell the difference between what is real and what is fake. In 2018, it was discovered how …		 Yisroel Mirsky; Wenke Lee; ACM Computing Surveys (CSUR) 2021-01-01
260 A Review on Social Spam Detection: Challenges, Open Issues, and Future Directions
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Abstract: Online Social Networks are perpetually evolving and used in plenteous applications such as content sharing, chatting, making friends/followers, customer engagements, commercials, …		 Sanjeev Rao; Anil K. Verma; Tarunpreet Bhatia; Expert Syst. Appl. 2021-01-01
261 Detection and Attribution of Synthetically Generated Deepfake Imagery
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Abstract: AI-generated deepfake images are a fast-growing challenge in media forensics, but conventional methods of recognising them are now inadequate and inappropriate. Identification and …		 Brandon Khoo; 2021-01-01
262 How Algorithms Create and Prevent Fake News: Exploring The Impacts of Social Media, Deepfakes, GPT-3, and More
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 Noah Giansiracusa; How Algorithms Create and Prevent Fake News 2021-01-01
263 Improving Generalization of Deepfake Detection with Domain Adaptive Batch Normalization
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Abstract: Deepfake, a well-known face forgery technique, has raised serious concerns about personal privacy and social media security. Therefore, a plenty of deepfake detection methods come …		 ZIXIN YIN et. al. Proceedings of the 1st International Workshop on … 2021-01-01
264 Challenges and Applications of Face Deepfake
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 Lamyanba Laishram; Md. Maklachur Rahman; Soon Ki Jung; 2021-01-01
265 Deepfake E Violência Verbal Nos Comentários De Internautas No Facebook
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Abstract: O objetivo deste trabalho é analisar a violência verbal em comentários de deepfake no Facebook. Partimos da hipótese de que, diante do discurso satírico e tendencioso de uma …		 Manoel Klebson de Andrade Oliveira; Andréa Moreira Gonçalves de Albuquerque; Dóris de Arruda Carneiro da Cunha; Cadernos de Linguística 2021-01-01
266 How Media Literacy Competency Contribute to Political Participation Integrity Among Young People
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Abstract: Emerging social media reported escalating political participation among young people in social media and stimulate their interest to become a media content producer. Young media …		 Siti Nurshahidah Sah Allam; Mohd Sufiean Hassan; Abdul Rauf Ridzuan; Rosilawati Sultan Mohideen; Ilya Yasnorizar Ilyas; International Journal of Academic Research in Business and … 2021-01-01
267 DmyT: Dummy Triplet Loss for Deepfake Detection
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Abstract: Recent progress in deep learning-based image generation has madeit easier to create convincing fake videos called deepfakes. Whilethe benefits of such technology are undeniable, …		 Nicolas Beuve; Wassim Hamidouche; Olivier Deforges; Proceedings of the 1st Workshop on Synthetic Multimedia – … 2021-01-01
268 Deepfake Detection Using Robust Spatial and Temporal Features from Facial Landmarks
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Abstract: Most current deepfake detectors may suffer the decrease of detection accuracy under common video processing like compression. To deal with this issue, we proposed a new way of …		 Meng Li; Beibei Liu; Yongjian Hu; Liepiao Zhang; Shiqi Wang; 2021 IEEE International Workshop on Biometrics and … 2021-01-01
269 «Deepfakes»: El Pròxim Repte En La Detecció De Notícies Falses
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Abstract: A deepfake is a hyper-realistic video, digitally manipulated to represent people saying or doing things that never really happened. With the sophistication of techniques for …		 Francisco José García-Ull; Anàlisi 2021-01-01
270 MTD-Net: Learning to Detect Deepfakes Images By Multi-Scale Texture Difference
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Abstract: With the rapid development of face manipulation technology, it is difficult for human eyes to distinguish fake face images. On the contrary, Convolutional Neural Network (CNN) …		 Jiachen Yang; Aiyun Li; Shuai Xiao; Wen Lu; Xinbo Gao; IEEE Transactions on Information Forensics and Security 2021-01-01
271 Deepfake Voices Can Fool Humans and Smart Assistants
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 Chris Stokel-Walker; New Scientist 2021-01-01
272 ON THE EXPANSION OF LEGAL REGULATION IN THE FIELD OF ARTIFICIAL INTELLIGENCE: DEEPFAKE AS A THREAT TO NATIONAL SECURITY
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 Alexander S. Kiselev; Bulletin of the Moscow State Regional University … 2021-01-01
273 Smart Watermark to Defend Against Deepfake Image Manipulation
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Abstract: Deepfake image manipulation has become a serious security threat to the social network. Currently, there are limited studies on protective methods that are against Deepfake image …		 Luochen Lv; 2021 IEEE 6th International Conference on Computer and … 2021-01-01
274 Deepfake E As Consequências Sociais Da Mecanização Da Desconfiança
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Abstract: O artigo tem origem em trabalhos anteriores que analisaram a crise de confiança na sociedade e como esta seria paradoxalmente impulsionada, em parte, por avanços como o …		 Lucia Santaella; Marcelo de Mattos Salgado; TECCOGS: Revista Digital de Tecnologias Cognitivas 2021-01-01
275 Potential Threat of Face Swapping to EKYC with Face Registration and Augmented Solution with Deepfake Detection
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 Trong-Le Do; Mai-Khiem Tran; Huy H. Nguyen; Minh-Triet Tran; 2021-01-01
276 The Distinct Wrong of Deepfakes
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Abstract: Deepfake technology presents significant ethical challenges. The ability to produce realistic looking and sounding video or audio files of people doing or saying things they did …		 Adrienne de Ruiter; Philosophy & Technology 2021-01-01
277 Face Swapping Consistency Transfer with Neural Identity Carrier
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Abstract: Deepfake aims to swap a face of an image with someone else’s likeness in a reasonable manner. Existing methods usually perform deepfake frame by frame, thus ignoring video …		 KUNLIN LIU et. al. Future Internet 2021-01-01
278 DeepfakeNet, An Efficient Deepfake Detection Method
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Abstract: Different CNNs models do not perform well in deepfake detection in cross datasets. This paper proposes a deepfake detection model called DeepfakeNet, which consists of 20 network …		 Dafeng Gong; Yogan Jaya Kumar; Ong Sing Goh; Zi Ye; Wanle Chi; 2021-01-01
279 Development and Application of Ethics Education STEAM Projects Using DeepFake Apps
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 Jung Hwang; Eunjeong Choe; Jeonghye Han; 2021-01-01
280 Deepfake Detection Using Capsule Networks and Long Short-Term Memory Networks
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Abstract: With the recent advancements of technology, and in particular with graphics processing and artificial intelligence algorithms, fake media generation has become easier. Using deep …		 Akul Mehra; Luuk J. Spreeuwers; Nicola Strisciuglio; 2021-01-01
281 Educational Responsibility in The Deepfake Era
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Abstract: Choosing the right technologies to match student learning outcomes in today’s technology-integrated classrooms presents educators with multiple instructional design challenges …		 Rebecca J. Blankenship; Deep Fakes, Fake News, and Misinformation in Online … 2021-01-01
282 Deepfake and Digital Citizenship
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Abstract: Artificial intelligence technologies are beneficial for individuals’ lives, but those technologies are also sources of mass and chaos. Due to hyper-realistic videos based on …		 Seda Gökçe Turan; Deep Fakes, Fake News, and Misinformation in Online … 2021-01-01
283 A Novel Extended Ripple and Cyberbullies Data Detection (E- RACYBDD) Framework to Mitigate Deep Fake Attacks on Social Media
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Abstract: Social media is one of the topmost communications in the present world and some discomforts are also hidden in this. Social media is one of the platforms for cyberbullying. …		 Bhimavarapu Usharani; Deep Fakes, Fake News, and Misinformation in Online … 2021-01-01
284 Animating Past Worlds
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Abstract: Creating ‘past worlds’ is more than just creative flair or technical wizardry, it is the distillation of grounded heritage interpretation and reflection as applied to the creative …		 William Michael Carter; Virtual Heritage: A Concise Guide 2021-01-01
285 Bots, Fake News, Fake Faces and Deepfakes: Automation, Under The Bias of Dromology, As A Sophisticated Form of Biopower to Influence The Democratic Election Process
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Abstract: This article intends to examine, under the influx of technological advances, new forms of fake news, fake faces, and deepfakes that have been disseminated by social bots, among …		 Jefferson Aparecido Dias; Heloisa Helou Doca; Fabiano Fernando da Silva; Pensar – Revista de Ciências Jurídicas 2021-01-01
286 Attention Guided Spatio-Temporal Artifacts Extraction for Deepfake Detection
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 Zhibing Wang; Xin Li; Rongrong Ni; Yao Zhao; 2021-01-01
287 Preventing DeepFake Attacks on Speaker Authentication By Dynamic Lip Movement Analysis
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Abstract: Recent research has demonstrated that lip-based speaker authentication systems can not only achieve good authentication performance but also guarantee liveness. However, with …		 Chen-Zhao Yang; Jun Ma; Shilin Wang; Alan Wee-Chung Liew; IEEE Transactions on Information Forensics and Security 2021-01-01
288 Don’t Trust Your Eyes: Image Manipulation in The Age of DeepFakes
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Abstract: We review the phenomenon of deepfakes, a novel technology enabling inexpensive manipulation of video material through the use of artificial intelligence, in the context of today’s …		 Johannes Langguth; Konstantin Pogorelov; Stefan Brenner; Petra Filkuková; Daniel Thilo Schroeder; 2021-01-01
289 A Review of Deepfake Technology: An Emerging AI Threat
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 Mridul Sharma; Mandeep Kaur; Advances in Intelligent Systems and Computing 2021-01-01
290 Deepfake Video Detection Using 3D-Attentional Inception Convolutional Neural Network
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 Changlei Lu; Bin Liu; Wenbo Zhou; Qi Chu; Nenghai Yu; 2021-01-01
291 A Multi-Layer Capsule-based Forensics Model for Fake Detection of Digital Visual Media
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Abstract: the dangers generated from synthesized multimedia are increasing every day. The creation of the so-called Deepfakes multimedia is vastly evolving, making the detection task harder …		 Samar Samir Khalil; Sherin M. Youssef; Sherine Nagy Saleh; 2020 International Conference on Communications, Signal … 2021-01-01
292 Deep Fake Recognition in Tweets Using Text Augmentation, Word Embeddings and Deep Learning
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 Senait Gebremichael Tesfagergish; Robertas Damasevicius; Jurgita Kapociute-Dzikiene; 2021-01-01
293 Deepfake Detection Using GAN Discriminators
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Abstract: Deepfake videos are videos where the features of a person are replaced with the features of another person. Videos can be manipulated using powerful Deep Learning techniques. This …		 Sai Ashrith Aduwala; Manish Arigala; Shivan Desai; Heng Jerry Quan; Magdalini Eirinaki; 2021 IEEE Seventh International Conference on Big Data … 2021-01-01
294 Detecting Manipulated Facial Videos: A Time Series Solution
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Abstract: We propose a new method to expose fake videos based on a time series solution. The method is based on bidirectional long short-term memory (Bi-LSTM) backbone architecture with two …		 Zhewei Zhang; Can Mal; Bowen Ding; Meilin Gao; 2020 25th International Conference on Pattern Recognition … 2021-01-01
295 Deepfake Fight: AI-Powered Disinformation and Perfidy Under The Geneva Conventions
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 David Allen; SSRN Electronic Journal 2021-01-01
296 Deepfake Detection By Exposing AI-Generated Fake Face Video
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 Anushree Deshmukh; Sunil Wankhade; 2021-01-01
297 Deep Fake Geography? When Geospatial Data Encounter Artificial Intelligence
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Abstract: The developing convergence of Artificial Intelligence and GIScience has raised a concern on the emergence of deep fake geography and its potentials in transforming human …		 Bo Zhao; Shaozeng Zhang; Chunxue Xu; Yifan Sun; Chengbin Deng; Cartography and Geographic Information Science 2021-01-01
298 Current Challenges and Applications of DeepFake Systems
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 Sandeep Bhat; Zeel Naman Shah; G. M. Siddesh; Computer Vision and Recognition Systems Using Machine and … 2021-01-01
299 Detecting Deep-Fake Videos from Aural and Oral Dynamics
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Abstract: A face-swap deep fake replaces a person’s face – from eyebrows to chin – with another face. A lip-sync deep fake replaces a person’s mouth region to be consistent with an …		 Shruti Agarwal; Hany Farid; 2021 IEEE/CVF Conference on Computer Vision and Pattern … 2021-01-01
300 Finding Facial Forgery Artifacts with Parts-Based Detectors
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Abstract: Manipulated videos, especially those where the identity of an individual has been modified using deep neural networks, are becoming an increasingly relevant threat in the modern …		 Steven Schwarcz; Rama Chellappa; 2021 IEEE/CVF Conference on Computer Vision and Pattern … 2021-01-01
301 Navigating The Maze: Deepfakes, Cognitive Ability, and Social Media News Skepticism
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Abstract: The early apprehensions about how deepfakes (also deep fakes) could be weaponized for social and political purposes are now coming to pass. This study is one of the first to …		 Saifuddin Ahmed; New Media & Society 2021-01-01
302 Numerical Analysis and Optimization Application Trend of Deepfake Video Inspection Technology
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Abstract: Science and technology can be a double-edged sword. Novel digital technologies not only allow people to enjoy more digital achievements, but also make it increasingly difficult to …		 Jiyu Xia; Man Hua; Journal of Physics: Conference Series 2021-01-01
303 Exposing DeepFake Videos Using Attention Based Convolutional LSTM Network
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Abstract: The detection of face tampering in videos created by artificial intelligence techniques (commonly known as the Deep Fakes) has become an important and challenging task in network …		 Yishan Su; Huawei Xia; Qi Liang; Weizhi Nie; Neural Process. Lett. 2021-01-01
304 Yeni Nesil Tehdit: Derin Kurgu (DeepFake)
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 Şebnem Özdemir; TRT Akademi 2021-01-01
305 Session Details: Session 2: Deepfake Generation
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 Weiling Chen; Proceedings of the 1st Workshop on Synthetic Multimedia – … 2021-01-01
306 Countermeasure Against Deepfake Using Steganography and Facial Detection
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Abstract: As deepfake technology continues to advance at a rapid pace, there is a constant need to develop new methods to counteract its use. Physical copies of authentic items like signed …		 Kyle Corcoran; Jacob Ressler; Ye Zhu; Journal of Computer and Communications 2021-01-01
307 Satellite Image Deep Fake Creation and Detection
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 Brandon Wachs; 2021-01-01
308 Exposing Fake Faces Through Deep Neural Networks Combining Content and Trace Feature Extractors
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Abstract: With the breakthrough of computer vision and deep learning, there has been a surge of realistic-looking fake face media manipulated by AI such as DeepFake or Face2Face that …		 Eunji Kim; Sungzoon Cho; IEEE Access 2021-01-01
309 Irmãs Siamesas Fake News E Pós-verdade Expandidas Nas Deepfakes
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Abstract: A partir de 2016, a expressão fake news e sua parceira, pós-verdade, passaram crescentemente a tomar conta das mídias noticiosas e interpretativas, com muitas matérias publicadas …		 Lucia Santaella; TECCOGS: Revista Digital de Tecnologias Cognitivas 2021-01-01
310 Channel-Wise Spatiotemporal Aggregation Technology for Face Video Forensics
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Abstract: Recent progress in deep learning, in particular the generative models, makes it easier to synthesize sophisticated forged faces in videos, leading to severe threats on social …		 Yujiang Lu; Yaju Liu; Jianwei Fei; Zhihua Xia; Secur. Commun. Networks 2021-01-01
311 Session Details: Session 1: Deepfake Detection
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 Pavel Korshunov; Proceedings of the 1st Workshop on Synthetic Multimedia – … 2021-01-01
312 DeepFake Videos Detection Based on Texture Features
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 Bozhi Xu; Jiarui Liu; Jifan Liang; Wei Lu; Yue Zhang; Cmc-computers Materials & Continua 2021-01-01
313 Demystifying Deepfakes Using Deep Learning
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Abstract: Manipulation of images, videos and audios using face edit apps and web services have long been in use, since decades but recent advances in deep learning has led to rising AI …		 Raj Kumar Singh; Prachi Vinod Sarda; Shruti Aggarwal; Dinesh Kumar Vishwakarma; 2021 5th International Conference on Computing … 2021-01-01
314 Deepfake
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Abstract: O termo “fake news” começou a povoar as mídias sociais, principalmente a partir de 2016, em função das eleições à presidência dos Estados Unidos. Estudos apontam que as notícias …		 Thaïs Helena Falcão Botelho; Winfried Nöth; TECCOGS: Revista Digital de Tecnologias Cognitivas 2021-01-01
315 CRIM’s System Description for The ASVSpoof2021 Challenge
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Abstract: In this paper, we provide description of our submitted systems to the ASVSpoof2021 Challenge logical access attack and audio deep fake tasks. The challenge provides a difficult …		 Woo Hyun Kang; Jahangir Alam; Abderrahim Fathan; 2021 Edition of the Automatic Speaker Verification and … 2021-01-01
316 Deepfakes Creation and Detection Using Deep Learning
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Abstract: Deep learning has been used in a wide range of applications like computer vision, natural language processing and image detection. The advancement in deep learning algorithms in …		 Hady A. Khalil; Shady A. Maged; 2021 International Mobile, Intelligent, and Ubiquitous … 2021-01-01
317 FOREIGN EXPERIENCE OF LEGAL REGULATION OF THE FIGHT AGAINST FAKE NEWS
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Abstract: The article describes the role of the Internet in the modern information society. The negative consequences of the openness of this information and telecommunications network are …		 Anna S. Zueva; Liana A. Makaeva; SCIENTIFIC REVIEW. SERIES 1. ECONOMICS AND LAW 2021-01-01
318 Beyond Deep Fakes: Conceptual Framework, Applications, and Research Agenda for Neural Rendering of Realistic Digital Faces
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 Mike Seymour; Kai Riemer; Lingyao Yuan; Alan R. Dennis; 2021-01-01
319 O Direito À Imagem Na Era Das Deep Fakes
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Abstract: O objetivo do presente artigo e analisar o direito a imagem na atualidade, tendo em vista os avancos tecnologicos, e se valendo do recurso a uma perspectiva que contemple a …		 Filipe Medon Medon; 2021-01-01
320 A Survey on Video-Based Fake News Detection Techniques
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Abstract: In today’s world, fake news identification is a critical problem. Fake news may exist in form of text, images and videos also. There are several techniques exist for fake news …		 Ronak Agrawal; Dilip Kumar Sharma; 2021 8th International Conference on Computing for … 2021-01-01
321 Spatio-Temporal Deepfake Detection with Deep Neural Networks
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 Andrey Sebyakin; Vladimir Soloviev; Anatoly Zolotaryuk; 2021-01-01
322 A Threat of Deepfakes As A Weapon on Digital Platform and Their Detection Methods
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Abstract: Advances in machine learning, deep learning, and Artificial Intelligence(AI) allows people to exchange other people’s faces and voices in videos to make it look like what they did …		 Manish Khichi; Rajesh Kumar Yadav; 2021 12th International Conference on Computing … 2021-01-01
323 Fake-buster: A Lightweight Solution for Deepfake Detection
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Abstract: Recent advances in video manipulation techniques have made synthetic media creation more accessible than ever before. Nowadays, video edition is so realistic that we cannot rely …		 Nathan Hubens; Matei Mancas; Bernard Gosselin; Marius Preda; Titus Zaharia; 2021-01-01
324 A Survey on Face Forgery Detection of Deepfake
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Abstract: With the development of internet technology, artificial intelligence shows its rapid growth in recent year as well. More and more people pay attention to this field, including …		 Ying Zhang; Feng Gao; Zichen Zhou; Hong Guo; 2021-01-01
325 Del Mito A La Viralidad
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Abstract: Tema principal del artículo: El mito es un recurso ancestral, tradicionalmente usado en la publicidad. El deepfake, una tecnología novedosa y polémica que abre un extraordinario …		 Isabel Palomo-Domínguez; aDResearch ESIC International Journal of Communication … 2021-01-01
326 Disinformation, Social Media, Bots, and Astroturfing: The Fourth Wave of Digital Democracy
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Abstract: This article reflects on the conceptualization and the salient features of the ecology of e-democracy. The authors identify four distinct waves marked by technological innovations …		 Berta García-Orosa; El Profesional de la información 2021-01-01
327 ASVspoof 2021: Accelerating Progress in Spoofed and Deepfake Speech Detection
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Abstract: ASVspoof 2021 is the fourth edition in the series of biannual challenges which aim to promote the study of spoofing and the design of countermeasures to protect automatic speaker …		 JUNICHI YAMAGISHI et. al. ArXiv 2021-01-01
328 Deep-faking It: Generating and Detecting Synthetic Remote Sensing Imagery
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Abstract: In the media forensics research community, there is a prevailing belief that within 3-5 years, deepfake video portraits (i.e. videos of people talking) will become realistic …		 Christopher X. Ren; Juston Moore; Amanda Ziemann; James Theiler; 2021-01-01
329 To Dally with Dalí: Deepfake (Inter)faces in The Art Museum
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Abstract: This essay focuses on the nascent symbiotic relationship between deepfakes and art museums and galleries, as demonstrated by three case studies. The first one, housed at the Dalí …		 Mihaela Mihailova; Convergence: The International Journal of Research into New … 2021-01-01
330 Social Media and Fake News Impact on Social Movements
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Abstract: Social media has become the favored digital communication channel and offers many advantages, such as spreading information faster than conventional media. However, social media’s …		 Heather C. Webb; Manal Emam; 2021-01-01
331 Detecting Deepfakes Using Deep Learning
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Abstract: Images play an important role in defining human perception, and the power to manipulate such images gives immense power to malicious users. The new advancement in Artificial …		 J C Dheeraj; Krutant Nandakumar; A V Aditya; B S Chethan; G C R Kartheek; 2021 International Conference on Recent Trends on … 2021-01-01
332 Deepfake Consumer Reviews in Tourism: Preliminary Findings
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 Aarni Tuomi; 2021-01-01
333 Known-unknown Data Augmentation Strategies for Detection of Logical Access, Physical Access and Speech Deepfake Attacks: ASVspoof 2021
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 Rohan Kumar Das; 2021 Edition of the Automatic Speaker Verification and … 2021-01-01
334 DeepFake Detection Using A Frame Based Approach Involving CNN
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Abstract: This paper proposes a novel model to detect Deep-Fakes, which are hyper-realistic fake videos generated by advanced AI algorithms involving facial superimposition. With a growing …		 Atmik Ajoy; Chethan U Mahindrakar; Dhanya Gowrish; Vinay A; 2021 Third International Conference on Inventive Research … 2021-01-01
335 Appearance Matters, So Does Audio: Revealing The Hidden Face Via Cross-Modality Transfer
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Abstract: Recently, there has been an exponential increase in the security concerns raised by faking face (e.g., deepfake), which automatically changes the identity with a specifically …		 CHENQI KONG et. al. IEEE Transactions on Circuits and Systems for Video … 2021-01-01
336 Deepfakes on Twitter: Which Actors Control Their Spread?
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Abstract: The term deepfake was first used in a Reddit post in 2017 to refer to videos manipulated using artificial intelligence techniques and since then it is becoming easier to create …		 Jesús Pérez Dasilva; Koldobika Meso Ayerdi; Terese Mendiguren Galdospin; Media and Communication 2021-01-01
337 Super-resolution Reconstruction Detection Method for DeepFake Hard Compressed Videos
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Abstract: The forensics methods of DeepFake video generally use convolution neural networks. However, these methods perform poorly on hard compressed DeepFake datasets and make a large …		 Lei Sun; Hongmeng Zhang; Xiuqing Mao; Song Guo; Yongjin Hu; 2021-01-01
338 Exposing Deepfake Videos By Tracking Eye Movements
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Abstract: It has recently become a major threat to the public media that fake videos are rapidly spreading over the Internet. The advent of Deepfake, a deep-learning based toolkit, has …		 Meng Li; Beibei Liu; Yongjian Hu; Yufei Wang; 2020 25th International Conference on Pattern Recognition … 2021-01-01
339 The Shallow of Your Smile: The Ethics of Expressive Vocal Deepfakes
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Abstract: Rapid technological advances in artificial intelligence are creating opportunities for real-time algorithmic modulations of a person’s facial and vocal expressions, or …		 N Guerouaou; G Vaiva; Jean-Julien Aucouturier; 2021-01-01
340 Specificity of Representation of Fake Information in Audiovisual Media Content
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Abstract: The situation of uncertainty, which became a consequence of the pandemic, forced the audience to actively interact with the media. This effect is especially noticeable in the …		 A. M. Shesterina; Vestnik NSU. Series: History and Philology 2021-01-01
341 Deepfake Video Detection with Facial Features and Long-Short Term Memory Deep Networks
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Abstract: Generative models have evolved immensely in the last few years. GAN-based video and image generation has become very accessible due to open source software available to anyone, …		 Dan-Cristian Stanciu; Bogdan Ionescu; 2021 International Symposium on Signals, Circuits and … 2021-01-01
342 Deepfake Video Detection By Using Convolutional Gated Recurrent Unit
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Abstract: Rapid development in deep learning is making it easier to create fake videos known as “deepfake” videos in which human faces are swapped. Since deepfake videos are difficult to …		 Yifeng Tu; Yang Liu; Xueming Li; 2021 13th International Conference on Machine Learning and … 2021-01-01
343 Deepfakes, Privacy, and Freedom of Speech
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Abstract: Inauthentic media depictions can harm a person’s privacy and reputation and pose a risk to broader society, as well. “Deepfake” technology allows the creation of a type of …		 Christa Laser; Eric Goldman; SSRN Electronic Journal 2021-01-01
344 Countering Spoof: Towards Detecting Deepfake with Multidimensional Biological Signals
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Abstract: The deepfake technology is conveniently abused with the low technology threshold, which may bring the huge social security risks. As GAN-based synthesis technology is becoming …		 Xinlei Jin; Dengpan Ye; Chuanxi Chen; Secur. Commun. Networks 2021-01-01
345 PRNU-based Deepfake Detection
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Abstract: As deepfakes become harder to detect by humans, more reliable detection methods are required to fight the spread of fake images and videos. In our work, we focus on PRNU-based …		 Florian Lugstein; Simon Baier; Gregor Bachinger; Andreas Uhl; Proceedings of the 2021 ACM Workshop on Information Hiding … 2021-01-01
346 Forensic Verification and Detection of Fake Video Using Deep Fake Algorithm
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Abstract: In the course of the most recen years, the ascent in cell phones and interpersonal organizations has made computerized pictures and recordings basic advanced articles. per …		 Prachi Kale; International Journal for Research in Applied Science and … 2021-01-01
347 Deepfake De Áudio
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Abstract: “Deepfake de áudio” faz parte do complexo de deepfakes, que é uma das formas das fake news (FN) que assolam o planeta no intuito de enganar os incautos. O objetivo do estudo é …		 Magaly Parreira do Prado; TECCOGS: Revista Digital de Tecnologias Cognitivas 2021-01-01
348 Combating Emerging Financial Risks in The Big Data Era: A Perspective Review
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Abstract: Big data technology has had a significant impact on new business and financial services: for example, GPS and Bluetooth inspire location-based services, and search and web …		 XUEQI CHENG et. al. Fundamental Research 2021-01-01
349 Fighting AI-synthesized Fake Media
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Abstract: Recent years have witnessed an unexpected and astonishing rise of AI-synthesized fake media (GAN synthesized faces, face-swap videos, and style transferred audios, commonly known …		 Siwei Lyu; Proceedings of the 1st Workshop on Synthetic Multimedia – … 2021-01-01
350 Beyond Deepfake Technology Fear: On Its Positive Uses for Livestock Farming
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Abstract: Deepfake technologies are known for the creation of forged celebrity pornography, face and voice swaps, and other fake media content. Despite the negative connotations the …		 Suresh Neethirajan; 2021-01-01
351 Recent Trends in Deepfake Detection
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Abstract: Almost 59% of the world’s population is on the internet, and in 2020, globally, there were more than 3.81 billion individual social network users. Eighty-six percent of the …		 Kerenalli Sudarshana; Mylarareddy C.; Advances in Computational Intelligence and Robotics 2021-01-01
352 Classification of Deepfake Videos Using Pre-trained Convolutional Neural Networks
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Abstract: The advancement of Artificial Intelligence (AI) has brought a revolution in the field of information technology. Furthermore, AI has empowered the new applications to run with …		 MOMINA MASOOD et. al. 2021 International Conference on Digital Futures and … 2021-01-01
353 FakeFilter: A Cross-distribution Deepfake Detection System with Domain Adaptation
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Abstract: Abuse of face swap techniques poses serious threats to the integrity and authenticity of digital visual media. More alarmingly, fake images or videos created by deep learning …		 JIANGUO JIANG et. al. J. Comput. Secur. 2021-01-01
354 DEEPFAKE AS A PHENOMENON OF POLITICAL COMMUNICATION
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Abstract: The development of digital technologies, coupled with progress in the development of self-learning programs based on AI (Artificial Intelligence), has obvious advantages in …		 М. Falaleev; N. Sitdikova; Е. Nechay; Transbaikal State University Journal 2021-01-01
355 An Effective Blockchain Technique to Resist Against Deepfake Videos
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Abstract: Recently, with growth in artificial intelligence as well in deep learning, fake digital data like images, footage, videos and audios has escalated. This fake data is termed as …		 Ujwal Patil; P.M. Chouragade; Premchand Ambhore; 2021 Third International Conference on Inventive Research … 2021-01-01
356 Deepfake: An Overview
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Abstract: Recent advancements in digital technologies have significantly increased the quality and capability to produce realistic images and videos using highly advanced computer graphics …		 Anupama Chadha; Vaibhav Kumar; Sonu Kashyap; Mayank Gupta; 2021-01-01
357 An Overview of Face Deep Forgery
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Abstract: With the rise of deep learning and the improvement of computer computing power, many interesting topics have emerged in the field of DeepFake in recent years. Deep forgery …		 Li Xinwei; Guo Jinlin; Chen Junnan; 2021 International Conference on Computer Engineering and … 2021-01-01
358 Blockchain Based Approach for Tackling Deepfake Videos
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Abstract: The technological advancements and a qualitative improvement in the field of artificial intelligence and deep learning leads to the creation of realistic-looking but phoney …		 Ujwal Patil; Prof. P. M. Chouragade; 2021-01-01
359 Global and Temporal-Frequency Attention Based Network in Audio Deepfake Detection
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 WANG CHENGLONG et. al. Journal of Computer Research and Development 2021-01-01
360 Automated Face Detection And Swapping In Video
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Abstract: Face was used as one of the normal behaviors for identifying humans. It generally stays a challenge to cause a computer framework to carry on like how a human senses things. We …		 Jayant Patel; Amruth Shetty; Amar Vishwakarma; Vishakha Shelke; 2021-01-01
361 Deep Fake : An Understanding of Fake Images and Videos
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Abstract: The Deepfake algorithm allows its user to create fake images, audios, videos that gives very real impression but is fake in real sense. This degree of technology is achieved due …		 Shweta Negi; Mydhili Jayachandran; Shikha Upadhyay; 2021-01-01
362 A Survey on Deepfake Video Detection
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 Peipeng Yu; Zhihua Xia; Jianwei Fei; Yujiang Lu; IET Biometrics 2021-01-01
363 ADGD’21: 1st Workshop on Synthetic Multimedia – Audiovisual Deepfake Generation and Detection
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Abstract: Deepfakes, i.e.synthetic or fake media content generated using deep learning, are a double-edged sword. On one hand, they pose new threats and risks in the form of scams, fraud, …		 Stefan Winkler; Weiling Chen; Abhinav Dhall; Pavel Korshunov; Proceedings of the 29th ACM International Conference on … 2021-01-01
364 Deepfake Detection Method Based on Cross-Domain Fusion
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Abstract: In recent years, despite its wide use in various fields, deepfake has been abused to generate hazardous contents such as fake movies, rumors, and fake news by manipulating or …		 Fang Sun; Niuniu Zhang; Pan Xu; Zengren Song; Security and Communication Networks 2021-01-01
365 Deepfake Videos Detection Based on Image Segmentation with Deep Neural Networks
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Abstract: With the rapid development of deep learning technology, videos with changed faces generated by deep neural networks (i.e., Deepfake videos) become more and more indistinguishable. …		 Yongjian Hu; Yifei Gao; Beibei Liu; Guangjun Liao; 2021-01-01
366 Proceedings of The 1st Workshop on Synthetic Multimedia – Audiovisual Deepfake Generation and Detection
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 Proceedings of the 1st Workshop on Synthetic Multimedia – … 2021-01-01
367 Comparison Multi Transfer Learning Models for Deep Fake Image Recognizer
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Abstract: The advancement of technologies nowadays cause image digitally to become essential as an official document many available applications used for image editing. Those applications …		 NUR AIZAH ROSLI et. al. 2021 3rd International Cyber Resilience Conference (CRC) 2021-01-01
368 Detection Analysis of DeepFake Technology By Reverse Engineering Approach (DREA) of Feature Matching
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 Sonya Burroughs; Kaushik Roy; Balakrishna Gokaraju; Khoa Luu; 2021-01-01
369 DeepFake Videos Detection Using Self-Supervised Decoupling Network
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Abstract: With wide applications of facial manipulation technology, fake images and videos are becoming a great public concern. Although existing methods for face forgery detection could …		 Jian Zhang; Jiangqun Ni; Hao Xie; 2021 IEEE International Conference on Multimedia and Expo … 2021-01-01
370 A Lightweight 3D Convolutional Neural Network for Deepfake Detection
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Abstract: The rapid development of DeepFake technologies has brought great challenges to the authenticity of video contents. It is of vital importance to develop DeepFake detection methods, …		 Jiarui Liu; Kaiman Zhu; Wei Lu; Xiangyang Luo; Xianfeng Zhao; International Journal of Intelligent Systems 2021-01-01
371 Deepfake Video Authentication Based on Blockchain
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Abstract: Nowadays, it is difficult to predict the information such as news and videos on the internet is real or not and people are increasingly sharing it on social media without thinking …		 Ujwal Patil; P.M. Chouragade; 2021 Second International Conference on Electronics and … 2021-01-01
372 Detecting and Combating Deep Fakes
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Abstract: On November 24, 2020, Dr. Hany Farid presented Detecting and Combating Deep Fakes at the 2020 CASIS West Coast Security Conference. The presentation was followed by a group panel …		 Hany Farid; 2021-01-01
373 Understanding The Association of Personal Outlook in Free Speech Regulation and The Risk of Being Mis/Disinformed
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Abstract: This paper explores the relationship between a person’s outlook on the issues of freedom of speech (IV1), fake news regulation (IV2), and the risk of him/her falling prey to …		 Kevin Matthe Caramancion; 2021 IEEE World AI IoT Congress (AIIoT) 2021-01-01
374 Fighting Deepfakes Using Body Language Analysis
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Abstract: Recent improvements in deepfake creation have made deepfake videos more realistic. Moreover, open-source software has made deepfake creation more accessible, which reduces the …		 Robail Yasrab; Wanqi Jiang; Adnan Riaz; 2021-01-01
375 Deepfake Warnings for Political Videos Increase Disbelief But Do Not Improve Discernment: Evidence from Two Experiments
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Abstract: Recent advances in machine learning have led to the development of the “deepfake,” a convincingly realistic, computer-generated video of a public figure saying something they have …		 John Ternovski; Joshua Kalla; Peter Michael Aronow; 2021-01-01
376 Optical Flow Based CNN for Detection of Unlearnt Deepfake Manipulations
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Abstract: A new phenomenon named Deepfakes constitutes a serious threat in video manipulation. AI-based technologies have provided easy-to-use methods to create extremely realistic videos. …		 Roberto Caldelli; Leonardo Galteri; Irene Amerini; Alberto Del Bimbo; Pattern Recognit. Lett. 2021-01-01
377 Deep Fakes, Fake News, and Misinformation in Online Teaching and Learning Technologies
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 Advances in Educational Technologies and Instructional … 2021-01-01
378 DeepFake Electrocardiograms: The Beginning of The End for Privacy Issues in Medicine
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Abstract: Big data is needed to implement personalized medicine, but privacy issues are a prevalent problem for collecting data and sharing them between researchers. A solution is synthetic …		 V. L. THAMBAWITA et. al. 2021-01-01
379 A Dataset of Photos and Videos for Digital Forensics Analysis Using Machine Learning Processing
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Abstract: Deepfake and manipulated digital photos and videos are being increasingly used in a myriad of cybercrimes. Ransomware, the dissemination of fake news, and digital …		 Sara Ferreira; Mário Antunes; Manuel Eduardo Correia; Data 2021-01-01
380 The Development of Fake News in The Post-Truth Age
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Abstract: This study aims to provide a complete picture of the development of fake news during this post-truth era. Authors use a qualitative approach with a literature study method. …		 Munadhil Abdul Muqsith; Rizky Ridho Pratomo; 2021-01-01
381 How Are Deepfake Videos Detected? An Initial User Study
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 Nyein Nyein Thaw; Thin July; Aye Nu Wai; Dion Hoe-Lian Goh; Alton Yeow-Kuan Chua; 2021-01-01
382 Media Forensics Considerations on DeepFake Detection with Hand-Crafted Features
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Abstract: DeepFake detection is a novel task for media forensics and is currently receiving a lot of research attention due to the threat these targeted video manipulations propose to the …		 Dennis Siegel; Christian Kraetzer; Stefan Seidlitz; Jana Dittmann; Journal of Imaging 2021-01-01
383 Anti-Counterfeit Handwritten Signature Via DCGAN with SGPD Network
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Abstract: In recent years, the growth of machine learning makes the computer can learn many things by using artificial intelligence. One method that is feared nowadays is the computer’s …		 Hendry; Daniel Herman Fredy Manongga; Yessica Nataliani; Theopilus Hermanus Wellem; 2021 7th International Conference on Applied System … 2021-01-01
384 Exposing Manipulated Photos and Videos in Digital Forensics Analysis
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Abstract: Tampered multimedia content is being increasingly used in a broad range of cybercrime activities. The spread of fake news, misinformation, digital kidnapping, and …		 Sara Ferreira; Mário Antunes; Manuel E. Correia; Journal of Imaging 2021-01-01
385 The Demographic Profile Most at Risk of Being Disinformed
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Abstract: This paper explores the relationship between a person’s demographic data, age (IV1), gender (IV2), ethnicity (IV3), and political ideology (IV4), and the risk of him/her falling …		 Kevin Matthe Caramancion; 2021 IEEE International IOT, Electronics and Mechatronics … 2021-01-01
386 FeatureTransfer: Unsupervised Domain Adaptation for Cross-Domain Deepfake Detection
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Abstract: Recently, various Deepfake detection methods have been proposed, and most of them are based on convolutional neural networks (CNNs). (ese detection methods suffer from overfitting …		 Baoying Chen; Shunquan Tan; Secur. Commun. Networks 2021-01-01
387 DISINFORMATION DECONSTRUCTEDCOGNITION SECURITY AND DIGITAL CONTROL
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Abstract: Fake News and Deepfakes have lately been highlighted in informative videos, research papers and literature reviews as tools for disinformation, along with filter bubble and echo …		 Maria Magdalena POPESCU; SERIES VII – SOCIAL SCIENCES AND LAW 2021-01-01
388 A Deep Learning Framework for Audio Deepfake Detection
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 Janavi Khochare; Chaitali Joshi; Bakul Yenarkar; Shraddha Suratkar; Faruk Kazi; Arabian Journal for Science and Engineering 2021-01-01
389 ON CONTROLLING FAKES, DEEPFAKES, FAKE ACCOUNTS IN THE INTERNET
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Abstract: In the information society, information is the basis for decision-making at all levels. However, in post-truth society, fake news and deepfakes are often created and distributed …		 N. R. Krasovskaya; A. A. Gulyaev; 2021-01-01
390 Fooled By The Fakes: Cognitive Differences in Perceived Claim Accuracy and Sharing Intention of Non-political Deepfakes
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Abstract: We examine how individual differences influence perceived accuracy of deepfake claims and sharing intention. Rather than political deepfakes, we use a non-political deepfake of a …		 Saifuddin Ahmed; Personality and Individual Differences 2021-01-01
391 Detecting Compressed Deepfake Videos in Social Networks Using Frame-Temporality Two-Stream Convolutional Network
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Abstract: The development of technologies that can generate Deepfake videos is expanding rapidly. These videos are easily synthesized without leaving obvious traces of manipulation. Though …		 Juan Hu; Xin Liao; Wei Wang; Zheng Qin; IEEE Transactions on Circuits and Systems for Video … 2021-01-01
392 Real Vs Fake Faces: DeepFakes and Face Morphing
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 Jacob L. Dameron; 2021-01-01
393 Deepfake Deception
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 Noah Giansiracusa; How Algorithms Create and Prevent Fake News 2021-01-01
394 Fighting Fake News: Two Stream Network for Deepfake Detection Via Learnable SRM
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Abstract: Benefitting from the development of deep generative networks, modern fake news generation methods called Deepfake rapidly go viral over the Internet, calling for efficient …		 Bing Han; Xiaoguang Han; Hua Zhang; Jingzhi Li; Xiaochun Cao; IEEE Transactions on Biometrics, Behavior, and Identity … 2021-01-01
395 DEEPFAKE DETECTION USING COMPUTER VISION
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Abstract: These days, a problem of AI-generated face swapping videos are rising rapidly, also known as the DeepFakes. These kinds of videos create threats to privacy of a specific person, …		 Ankit Mishra; Aman Verma; Arunav Dey; Abhay Singh; International Journal of Engineering Applied Sciences and … 2021-01-01
396 Multi-Layer Fusion Neural Network for Deepfake Detection
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Abstract: Recently, the spread of videos forged by deepfake tools has been widely concerning, and effective ways for detecting them are urgently needed. It is known that such artificial …		 Zheng Zhao; Penghui Wang; Wei Lu; International Journal of Digital Crime and Forensics 2021-01-01
397 Spotting Deepfakes and Face Manipulations By Fusing Features from Multi-Stream CNNs Models
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Abstract: Deepfake is one of the applications that is deemed harmful. Deepfakes are a sort of image or video manipulation in which a person’s image is changed or swapped with that of …		 Semih Yavuzkilic; Abdulkadir Sengür; Zahid Akhtar; Kamran Siddique; Symmetry 2021-01-01
398 DISSIMILAR: Towards Fake News Detection Using Information Hiding, Signal Processing and Machine Learning
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Abstract: Digital media have changed the classical model of mass media that considers the transmitter of a message and a passive receiver, to a model where users of the digital media can …		 David Megías; Minoru Kuribayashi; Andrea Rosales; Wojciech Mazurczyk; The 16th International Conference on Availability, … 2021-01-01
399 Deepfake Detection Using Rationale-augmented Convolutional Neural Network
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Abstract: Deepfake network is a prominent topic of research as an application to various systems about security measures. Although there have been many recent advancements in facial …		 Saadaldeen Rashid Ahmed Ahmed; Emrullah Sonuç; Applied Nanoscience 2021-01-01
400 Deep Neural Networks Based Error Level Analysis for Lossless Image Compression Based Forgery Detection
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Abstract: The proposed model is implemented in deep learning based on counterfeit feature extraction and Error Level Analysis (ELA) techniques. Error level analysis is used to improve the …		 Chintakrindi Geaya Sri; Shahana Bano; Tinnavalli Deepika; Nehanth Kola; Yerramreddy Lakshmi Pranathi; 2021 International Conference on Intelligent Technologies … 2021-01-01
401 Learning Diverse Local Patterns for Deepfake Detection with Image-level Supervision
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Abstract: To prevent the Deepfake-like videos from spreading, researchers have proposed many anti-forgery methods. However, most approaches require pixel-wise annotation, which conflicts …		 HEFEI LING et. al. 2021 International Joint Conference on Neural Networks … 2021-01-01
402 Deepfakes and Face Swapping
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Abstract: There is likely no more controversial application of generative modeling than creating fake faces or applying the technology to swap faces. This technique is colloquially known as …		 Micheal Lanham; Generating a New Reality 2021-01-01
403 Deepfake Privacy: Attitudes and Regulation
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Abstract: Using only a series of images of a person’s face and publicly available software, it is now possible to insert the person into a video and show them saying or doing almost …		 Matthew B. Kugler; Carly Pace; SSRN Electronic Journal 2021-01-01
404 Data Ethics: Facts and Fiction
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Abstract: Data ethics is probably one of the areas in big data with most compelling questions still unanswered. With technology advancing at a rapid paste the danger is for data ethics to …		 Fady A. Harfoush; Management for Professionals 2021-01-01
405 The Deep Realness of Deepfake Pornography:
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 Shaka McGlotten; Susanna Paasonen; Jonh Paul Stadler; 2021-01-01
406 Deepfake and Security of Video Conferences
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Abstract: Deep learning is widely used to create artificial contents on the Internet. Similarly, it is also used to detect fake contents. Fake frames created and integrated with deep …		 AHMET SEMIH UÇAN et. al. 2021 6th International Conference on Computer Science and … 2021-01-01
407 Latent Vector Recovery of Audio GANs with Application in Deepfake Audio Detection
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Abstract: Advanced Generative Adversarial Networks (GANs) are remarkable in generating in- telligible audio from a random latent vector. In this paper, we examine the task of recovering the …		 Nicky Bayat; Vahid Reza Khazaie; Andrew Keyes; Yalda Mohsenzadeh; Proceedings of the Canadian Conference on Artificial … 2021-01-01
408 Detecting Deepfake Videos By Visual-audio Synchronism: Work-in-progress
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Abstract: Different to traditional works on frame-level features and temporal characteristics, we propose a deepfake video detection method based on visual-audio synchronism, which compares …		 Zhufeng Fan; Jinyu Zhan; Wei Jiang; Proceedings of the 2021 International Conference on … 2021-01-01
409 Learning Spatio-temporal Features to Detect Manipulated Facial Videos Created By The Deepfake Techniques
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 Nguyen Xuan Hau; Thai Son Tran; Le Van Thinh; Kim Duy Nguyen; Dinhtu Truong; Digit. Investig. 2021-01-01
410 High Performance DeepFake Video Detection on CNN-Based with Attention Target-Specific Regions and Manual Distillation Extraction
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Abstract: The rapid development of deep learning models that can produce and synthesize hyper-realistic videos are known as DeepFakes. Moreover, the growth of forgery data has prompted …		 Van-Nhan Tran; Suk-Hwan Lee; Hoanh-Su Le; Ki-Ryong Kwon; Applied Sciences 2021-01-01
411 Detection of Machine Generated Multimedia Elements Using Deep Learning
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Abstract: With recent advancements in Deep Learning [DL], many useful tasks like text recognition, speech recognition and many more tasks can be easily done using machines. Such …		 Bhavik Sanghvi; Harsh Shelar; Mridul Pandey; Jignesh Sisodia; 2021 5th International Conference on Computing … 2021-01-01
412 Deepfakes Na Perspectiva Da Semiótica
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Abstract: As notícias falsas são uma arma de desinformação conhecida, capaz de ameaçar o estado democrático. Em um momento em que as mídias tradicionais são constantemente atacadas e …		 Carlos Eduardo de Souza; Lucia Santaella; TECCOGS: Revista Digital de Tecnologias Cognitivas 2021-01-01
413 The Shallow of Your Smile: The Ethics of Expressive Vocal Deepfakes
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Abstract: Rapid technological advances in artificial intelligence are creating opportunities for real-time algorithmic modulations of a person’s facial and vocal expressions, or “deep- …		 Nadia Guerouaou; Guillaume Vaiva; Jean-Julien Aucouturier; 2021-01-01
414 Deepfake Phenomenon: An Advanced Form of Fake News and Its Implications on Reliable Journalism
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Abstract: In the era of distinctly new media-marked social reality, the process of dissemination of disinformation is becoming an increasingly common phenomenon. As one of the technological …		 Amina Vatreš; Društvene i humanističke studije (Online) 2021-01-01
415 Exposing Vulnerabilities of Deepfake Detection Systems with Robust Attacks
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Abstract: Recent advances in video manipulation techniques have made the generation of fake videos more accessible than ever before. Manipulated videos can fuel disinformation and reduce …		 SHEHZEEN HUSSAIN et. al. Digital Threats: Research and Practice 2021-01-01
416 Visually Maintained Image Disturbance Against Deepfake Face Swapping
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 Junhao Dong; Xiaohua Xie; 2021 IEEE International Conference on Multimedia and Expo … 2021-01-01
417 Dispelling Misconceptions and Characterizing The Failings of Deepfake Detection
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 Neal Mangaokar; Atul Prakash; IEEE Security & Privacy 2021-01-01
418 ICaps-Dfake: An Integrated Capsule-Based Model for Deepfake Image and Video Detection
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Abstract: Fake media is spreading like wildfire all over the internet as a result of the great advancement in deepfake creation tools and the huge interest researchers and corporations are …		 Samar Samir Khalil; Sherin M. Youssef; Sherine Nagy Saleh; Future Internet 2021-01-01
419 Deepfake E A Realidade Sintetizada
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Abstract: As tecnologias deepfake, nas quais os computadores são treinados para manipular áudio e imagens em um nível cada vez mais impressionante de perfeição, estão sendo desenvolvidas a …		 Patrícia Fonseca Fanaya; TECCOGS: Revista Digital de Tecnologias Cognitivas 2021-01-01
420 Detection of Deepfake Images Created Using Generative Adversarial Networks: A Review
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 K. Remya Revi; K. R. Vidya; M. Wilscy; 2021-01-01
421 DeepFake Creation and Detection:A Survey
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Abstract: DeepFake has become very popular of late. The term DeepFake refers to any multimedia content generated using deep learning technology appearing realistic to people. Despite the …		 Swathi P; Saritha Sk; 2021 Third International Conference on Inventive Research … 2021-01-01
422 ADD: Attention-Based DeepFake Detection Approach
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Abstract: Recent advancements of Generative Adversarial Networks (GANs) pose emerging yet serious privacy risks threatening digital media’s integrity and trustworthiness, specifically …		 Aminollah Khormali; Jiann-Shiun Yuan; Big Data and Cognitive Computing 2021-01-01
423 Swapping Face Images with Generative Neural Networks for Deepfake Technology – Experimental Study
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 Michał Zendran; Andrzej Rusiecki; 2021-01-01
424 Surface Nanostructures for Engineering Spectral Responsivity in Imaging Sensor Pixels for Combatting Deepfakes
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Abstract: Current deepfake production methods use auto-encoders augmented by a generative adversarial network (GAN) to create fraudulent but convincing video footage. Developing neural …		 Lisa McPhillips; Ahasan Ahamed; Cesar Bartolo-Perez; M. Saif Islam; 2021-01-01
425 Deepfake Portrait Image Generation
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Abstract: With the prevailing of deep learning technology, especially generative adversarial networks (GAN), generating photo-realistic facial images has made huge progress. Image …		 Jianfei Cai; Proceedings of the 1st Workshop on Synthetic Multimedia – … 2021-01-01
426 A Machine Learning Based Approach for Deepfake Detection in Social Media Through Key Video Frame Extraction
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Abstract: Received:01 October 2020 /Accepted:date Abstract In the last few years, with the advent of deepfake videos, image forgery has become a serious threat. In a deepfake video, a …		 Alakananda Mitra; Saraju P. Mohanty; Peter Corcoran; Elias Kougianos; SN Comput. Sci. 2021-01-01
427 La Minaccia Del Deepfake Ed I Rischi Per La Cybersecurity Delle Organizzazioni Economiche : Un Approccio Pratico
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 Elisabetta Stringhi; 2021-01-01
428 Deepfake Video Detection By Combining Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN)
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Abstract: Nowadays, people are facing an emerging problem called deepfake videos. These videos were created using deep learning technology. Some are created just for fun, while others are …		 Yunes Al-Dhabi; Shuang Zhang; 2021 IEEE International Conference on Computer Science, … 2021-01-01
429 A Fight Against The Dissemination of Deepfakes in Other Countries: Criminal and Criminological Aspects
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Abstract: The authors analyze a relatively new phenomenon of spreading realistic audiovisual fake materials (deepfakes). This socially dangerous phenomenon is not reflected in the Russian …		 Roman Dremliuga; Alexander Korobeev; Russian Journal of Criminology 2021-01-01
430 EEG-Based Analysis of The Impact of Familiarity in The Perception of Deepfake Videos
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 Jan-Philipp Tauscher; Susana Castillo; Sebastian Bosse; Marcus Magnor; 2021-01-01
431 DeepFake Face Video Detection Using Hybrid Deep Residual Networks and LSTM Architecture
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 Semih Yavuzkiliç; Zahid Akhtar; Abdulkadir Sengür; Kamran Siddique; 2021-01-01
432 Soluciones Educativas Frente A Los Dilemas Éticos Del Uso De La Tecnología Deep Fake
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Abstract: Profound falsifications constitute a technological evolution, which expresses a radical change in the way information is produced, disseminated and inadequately used. This could …		 Mario González Arencibia; Dagmaris Martínez Cardero; 2021-01-01
433 Fake Face Detection Via Adaptive Manipulation Traces Extraction Network
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Abstract: With the proliferation of face image manipulation (FIM) techniques such as Face2Face and Deepfake, more fake face images are spreading over the internet, which brings serious …		 Zhiqing Guo; Gaobo Yang; Jiyou Chen; Xingming Sun; Comput. Vis. Image Underst. 2021-01-01
434 Eff-YNet: A Dual Task Network for DeepFake Detection and Segmentation
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Abstract: Advances in generative models and manipulation techniques have given rise to digitally altered videos known as deepfakes. These videos are difficult to identify for both humans …		 Eric Tjon; Melody Moh; Teng-Sheng Moh; 2021 15th International Conference on Ubiquitous … 2021-01-01
435 Awareness to Deepfake: A Resistance Mechanism to Deepfake
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Abstract: The goal of this study is to find whether exposure to Deepfake videos makes people better at detecting Deepfake videos and whether it is a better strategy against fighting …		 Mohammad Faisal Bin Ahmed; M. Saef Ullah Miah; Abhijit Bhowmik; Juniada Binti Sulaiman; 2021 International Congress of Advanced Technology and … 2021-01-01
436 From Deepfake to Psychotherapy: The Aesthetics of Deception Today
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 The Ancient Aesthetics of Deception 2021-01-01
437 Generative Adversarial Neural Networks and Deep Learning: Successful Cases and Advanced Approaches
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Abstract: Cross-domain artificial intelligence (AI) frameworks are the keys to amplify progress in science. Cutting edge deep learning methods offer novel opportunities for retrieving, …		 Oleksandr Striuk; Yuriy P. Kondratenko; International Journal of Computing 2021-01-01
438 Protecting The Global Information Space in Times of Armed Conflict
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Abstract: The legal implications of digital information warfare in the context of armed conflict have so far received only scarce attention. This paper aims at filling this gap by exposing …		 Robin Geiss; Henning Lahmann; Social Science Research Network 2021-01-01
439 Improving The Generalization Ability of Deepfake Detection Via Disentangled Representation Learning
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 Jiashang Hu; Shilin Wang; Xiaoyong Li; 2021-01-01
440 An Experimental Evaluation of Recent Face Recognition Losses for Deepfake Detection
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Abstract: Due to the recent breakthroughs of deep generative models, the fake faces, also known as deepfake which has been abused to deceive the general public, can be easily produced at …		 Yu-Cheng Liu; Chia-Ming Chang; I-Hsuan Chen; Yu-Ru Ku; Jun-Cheng Chen; 2020 25th International Conference on Pattern Recognition … 2021-01-01
441 DeepFake Detection Using 3D-Xception Net with Discrete Fourier Transformation
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Abstract: The videos are more popular for sharing content on social media to capture the audience’s attention. The artificial manipulation of videos is growing rapidly to make the videos …		 Adeep Biswas; Debayan Bhattacharya; Anil Kumar Kakelli; Journal of Information Systems and Telecommunication 2021-01-01
442 Fake Video Detection Using Modified XceptionNet
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Abstract: Deepfakes are a significant threat to society from the development of deep learning technology, which is able to manipulate digital images. Manipulated digital content potentially …		 Imam Kusniadi; Arief Setyanto; 2021 4th International Conference on Information and … 2021-01-01
443 EfficientNets for DeepFake Detection: Comparison of Pretrained Models
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Abstract: Rapid advances in media generation techniques have made the creation of AI-generated fake face videos more accessible than ever before. In order to accelerate the development of …		 Artem A. Pokroy; Alexey D. Egorov; 2021 IEEE Conference of Russian Young Researchers in … 2021-01-01
444 A Novel Approach to Detect Low Quality Deepfake Videos
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 Neeraj Guhagarkar; Sanjana Desai; Swanand Vaishampayan; Ashwini Save; Advances in Intelligent Systems and Computing 2021-01-01
445 Context-Aware Deep Markov Random Fields for Fake News Detection
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Abstract: Fake news is a serious problem, which has received considerable attention from both industry and academic communities. Over the past years, many fake news detection approaches …		 Tien Huu Do; Marc Berneman; Jasabanta Patro; Giannis Bekoulis; Nikos Deligiannis; IEEE Access 2021-01-01
446 BerConvoNet: A Deep Learning Framework for Fake News Classification
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Abstract: Fake news has become a major concern over the Internet. It influences people directly and should be identified. In the recent years, various Machine Learning (ML) and Deep …		 Monika Choudhary; Satyendra Singh Chouhan; Emmanuel S. Pilli; Santosh Kumar Vipparthi; Appl. Soft Comput. 2021-01-01
447 Deep4SNet: Deep Learning for Fake Speech Classification
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Abstract: Fake speech consists on voice recordings created even by artificial intelligence or signal processing techniques. Among the methods for generating false voice recordings are Deep …		 L. DoraM.Ballesteros; Yohanna Rodríguez-Ortega; Diego Renza; Gonzalo Arce; Expert Syst. Appl. 2021-01-01
448 Coordinated Topology Attacks in Smart Grid Using Deep Reinforcement Learning
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Abstract: In this article, we investigate the coordinated topology attacks in smart grid, which combine a physical topology attack and a cyber-topology attack. The physical attack first …		 Zhenhua Wang; Haibo He; Zhiqiang Wan; Yan Sun; IEEE Transactions on Industrial Informatics 2021-01-01
449 Efficiencies of Feature Engineering in The Machine Learning Approach for Fake News Classification
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Abstract: The rapid infiltration of fake news is a flaw to the otherwise valuable internet, a virtually global network that allows for the simultaneous exchange of information. While a …		 Katrin Donetski; 2021-01-01
450 Deep Learning for Fake News Detection in A Pairwise Textual Input Schema
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Abstract: In the past decade, the rapid spread of large volumes of online information among an increasing number of social network users is observed. It is a phenomenon that has often been …		 Despoina Mouratidis; Maria Nefeli Nikiforos; Katia Kermanidis; Comput. 2021-01-01
451 Pursuit for Authentic News Using Machine Learning Models
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Abstract: Fake news spreads like a wildfire and this is a big issue in this era. The Online world contains lots of fake news from various sources and channels by political parties, …		 Rahul Pradhan; Pragya Tiwary; Piyush Agarwal; Dilip Kumar Sharma; 2021 9th International Conference on Reliability, Infocom … 2021-01-01
452 Deep Contextualized Text Representation and Learning for Fake News Detection
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Abstract: In recent years, due to the widespread use of social media and broadcasting agencies around the world, people are extremely exposed to being affected by false information and fake …		 Mohammadreza Samadi; Maryam Mousavian; Saeedeh Momtazi; Inf. Process. Manag. 2021-01-01
453 Faultless Decision Making for False Information in Online: A Systematic Approach
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Abstract: An identifying the news are real or fake instantly with high accuracy is a challenging work. The deep learning algorithm is implementing here to acquire very accurate separation …		 Yasir Babiker Hamdan; Sathish; 2021-01-01
454 Discrepancy Detection Between Actual User Reviews and Numeric Ratings of Google App Store Using Deep Learning
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Abstract: Nowadays online reviews play a significant role in influencing the decision of consumers. Consumers show their experience and information about product quality in their reviews. …		 SAIMA SADIQ et. al. Expert Syst. Appl. 2021-01-01
455 Deep Learning and Natural Language Processing for Fake News Detection: A Survey
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Abstract: Fake news is a term which deals with fallacy in information, content or some sort of statistics or facts revealed to public for some sort of attention, to abuse someone as a means …		 Aditya Chokshi; Rejo Mathew; Social Science Research Network 2021-01-01
456 Comparison of Fake News Detection Using Machine Learning and Deep Learning Techniques
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Abstract: Fake news has spread widely on the Web in recent years due to the massive amount of information exchanged on digital media. This has motivates our study to determine the …		 Saeed Amer Alameri; Masnizah Mohd; 2021 3rd International Cyber Resilience Conference (CRC) 2021-01-01
457 Fake News Detection in Slovak Language Using Deep Learning Techniques
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Abstract: In recent years, the spreading of fake news presents a serious issue in the online environment. Automatic methods able to identify them from the text are being massively explored …		 Klaudia Ivancová; Martin Sarnovský; Viera Maslej-Krcšñáková; 2021 IEEE 19th World Symposium on Applied Machine … 2021-01-01
458 Fake Speech Answer-Sheet Detection on Intelligent Learning App Based on Block-Based Deep Neural Network
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Abstract: In order to detect the fake speech answer-sheet obtained by text-to-speech or voice conversion algorithms on intelligent English learning app, this paper proposed the idea of …		 Qingzhu Wu; Haitao Guo; Shaowei Xiong; Journal of Physics: Conference Series 2021-01-01
459 A Novel Stacking Approach for Accurate Detection of Fake News
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Abstract: With the increasing popularity of social media, people has changed the way they access news. News online has become the major source of information for people. However, much …		 Tao Jiang; Jian Ping Li; Amin Ul Haq; Abdus Saboor; Amjad Ali; IEEE Access 2021-01-01
460 Image Forgery Over Social Media Platforms – A Deep Learning Approach for Its Detection and Localization
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Abstract: Social media platforms play a significant role in spreading news in the current digital era. However, they have also been spreading fake images. Forged images posted on social …		 Bhuvanesh Singh; Dilip Kumar Sharma; 2021 8th International Conference on Computing for … 2021-01-01
461 A Deep Learning Based Approach for Classification of News As Real or Fake
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Abstract: In recent past, the growth of both the printed and digital media has greatly facilitated the business and the society. On account of the reach of social media, even the smallest …		 Juhi Kumari; Raman Choudhary; Swadha Kumari; Gopal Krishna; 2021-01-01
462 Deep Ensemble Approach for COVID-19 Fake News Detection from Social Media
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Abstract: Social media networks such as Facebook and Twitter are overwhelmed with COVID-19-related posts during the outbreak. People have also posted several fake news among the massive …		 Anu Priya; Abhinav Kumar; 2021 8th International Conference on Signal Processing and … 2021-01-01
463 Fake Job Detection and Analysis Using Machine Learning and Deep Learning Algorithms
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Abstract: With the pandemic situation, there is a strong rise in the number of online jobs posted on the internet in various job portals. But some of the jobs being posted online are …		 C. S. Anita; P. Nagarajan; G. Aditya Sairam; P. Ganesh; G. Deepakkumar; 2021-01-01
464 Deep Learning for Fake News Detection on Twitter Regarding The 2019 Hong Kong Protests
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Abstract: The dissemination of fake news on social media platforms is an issue of considerable interest, as it can be used to misinform people or lead them astray, which is particularly …		 ALEXANDROS ZERVOPOULOS et. al. Neural Computing and Applications 2021-01-01
465 Fake Detect: A Deep Learning Ensemble Model for Fake News Detection
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Abstract: Pervasive usage and the development of social media networks have provided the platform for the fake news to spread fast among people. Fake news often misleads people and creates …		 Nida Aslam; Irfan Ullah Khan; Farah Salem Alotaibi; Lama Abdulaziz Aldaej; Asma Khaled Aldubaikil; Complex. 2021-01-01
466 Synthesis of Glioma Histopathology Images Using Generative Adversarial Networks
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Abstract: Deep learning, a subset of artificial intelligence, has shown great potential in several recent applications to pathology. These have mainly involved the use of classifiers to …		 AB Levine; J Peng; SJM Jones; A Bashashati; S Yip; Canadian Journal of Neurological Sciences / Journal … 2021-01-01
467 Developing A Fake News Identification Model with Advanced Deep Language Transformers for Turkish Covid-19 Misinformation Data
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Abstract: The massive use of social media causes rapid information dissemination that amplifies harmful messages 4 such as fake news. Fake-news is misleading information presented as …		 Mehmet BOZUYLA; Akın ÖZÇİFT; TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER … 2021-01-01
468 A Comparative Study on Fake Job Post Prediction Using Different Data Mining Techniques
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Abstract: In recent years, due to advancement in modern technology and social communication, advertising new job posts has become very common issue in the present world. So, fake job …		 Sultana Umme Habiba; Md. Khairul Islam; Farzana Tasnim; 2021 2nd International Conference on Robotics, Electrical … 2021-01-01
469 Analysis of Text Feature Extractors Using Deep Learning on Fake News
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Abstract: Social media and easy internet access have allowed the instant sharing of news, ideas, and information on a global scale. However, rapid spread and instant access to …		 B. Ahmed; G. Ali; A. Hussain; A. Baseer; J. Ahmed; Engineering, Technology & Applied Science Research 2021-01-01
470 Advanced Art Authentication in Oil Paintings Using Precise 3D Morphological Analysis of Craquelure Patterns
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Abstract: The development of scientific technology for art authentication have elicited multidimensional evidence to distinguish forgeries from original artworks. Here, we analyzed …		 SOOJUNG KIM et. al. 2021-01-01
471 Fake News Detection Using Unsupervised and Deep Learning Algorithms
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Abstract: A detection of fake news is difficult due to limited publicly available resources (Datasets). Fake news is a false information which present in news or stories, blog so on. Fake …		 Asha. J; Meenakowshalya. A; Journal of Computer Science Engineering and Software Testing 2021-01-01
472 A Systematic Survey on Deep Learning and Machine Learning Approaches of Fake News Detection in The Pre- and Post-COVID-19 Pandemic
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Abstract: PurposeThe rapid advancement of technology in online communication and fingertip access to the Internet has resulted in the expedited dissemination of fake news to engage a global …		 Rajshree Varma; Yugandhara Verma; Priya Vijayvargiya; Prathamesh P. Churi; International Journal of Intelligent Computing and … 2021-01-01
473 Faking Signals to Fool Deep Neural Networks in AMC Via Few Data Points
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Abstract: The recent years has witnessed a rapid development of Deep Learning (DL) based Automation Modulation Classification (AMC) methods, which has proved to outperform traditional …		 HONGBIN MA et. al. IEEE Access 2021-01-01
474 Forged Face Detection Using ELA and Deep Learning Techniques
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Abstract: The paper presents real and fake facial image recognition using Deep learning and CNN. Image forgery recognition becomes a difficult task to find the authenticity of an image with …		 Qurat-ul-ain; Nudrat Nida; Aun Irtaza; Nouman Ilyas; 2021 International Bhurban Conference on Applied Sciences … 2021-01-01
475 A Deep Hybrid Learning Approach to Detect Bangla Fake News
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Abstract: Fake news has become a genuine threat to political, economical, religious and social stability. Although there has been an enormous amount of study done on the detection of fake …		 QUAZI ADIBUR RAHMAN ADIB et. al. 2021 5th International Symposium on Multidisciplinary … 2021-01-01
476 Identifying Invariant Texture Violation For Robust Deepfake Detection
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Highlight: To improve the robustness for high-realism fake data, we propose the Invariant Texture Learning (InTeLe) framework, which only accesses the published dataset with low visual quality.		 Xinwei Sun; Botong Wu; Wei Chen; arxiv-cs.CV 2020-12-18
477 Interdisciplinary Lessons Learned While Researching Fake News
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Abstract: The misleading and propagandistic tendencies in American news reporting have been a part of public discussion from its earliest days as a republic (Innis, 2007; Sheppard, 2007). …		 CHAR SAMPLE et. al. Frontiers in psychology 2020-12-16
478 Responsible Disclosure of Generative Models Using Scalable Fingerprinting
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Highlight: Over the past years, deep generative models have achieved a new level of performance.		 Ning Yu; Vladislav Skripniuk; Dingfan Chen; Larry Davis; Mario Fritz; arxiv-cs.CR 2020-12-15
479 The Emerging Threats Of Deepfake Attacks And Countermeasures
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Highlight: This paper provides an overview of deepfakes, their benefits to society, and how DT works.		 Shadrack Awah Buo; arxiv-cs.CR 2020-12-14
480 Identity-Driven DeepFake Detection
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Highlight: In this work, we present an alternative approach: Identity-Driven DeepFake Detection.		 XIAOYI DONG et. al. arxiv-cs.CV 2020-12-07
481 Cost Sensitive Optimization Of Deepfake Detector
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Highlight: In the present work, we concentrate on the so-called deepfake videos, where the source face is swapped with the targets.		 Ivan Kukanov; Janne Karttunen; Hannu Sillanpää; Ville Hautamäki; arxiv-cs.CV 2020-12-07
482 Technology-driven Alteration Of Nonverbal Cues And Its Effects On Negotiation
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Highlight: In this article, we look at some state-of-the-art technological advances that can enable such explicit and implicit alteration of nonverbal cues.		 Raiyan Abdul Baten; Ehsan Hoque; arxiv-cs.HC 2020-12-07
483 ID-Reveal: Identity-aware DeepFake Video Detection
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Highlight: To this end, we introduce ID-Reveal, a new approach that learns temporal facial features, specific of how a person moves while talking, by means of metric learning coupled with an adversarial training strategy.		 Davide Cozzolino; Andreas Rössler; Justus Thies; Matthias Nießner; Luisa Verdoliva; arxiv-cs.CV 2020-12-04
484 Adversarial Threats To DeepFake Detection: A Practical Perspective
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Highlight: In this work, we study the vulnerabilities of state-of-the-art DeepFake detection methods from a practical stand point.		 Paarth Neekhara; Brian Dolhansky; Joanna Bitton; Cristian Canton Ferrer; arxiv-cs.CV 2020-11-19
485 Training Strategies And Data Augmentations In CNN-based DeepFake Video Detection
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Highlight: In this paper we analyze how different training strategies and data augmentation techniques affect CNN-based deepfake detectors when training and testing on the same dataset or across different datasets.		 Luca Bondi; Edoardo Daniele Cannas; Paolo Bestagini; Stefano Tubaro; arxiv-cs.CV 2020-11-16
486 Neural Deepfake Detection With Factual Structure Of Text
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Highlight: To address this, we propose a graph-based model that utilizes the factual structure of a document for deepfake detection of text.		 WANJUN ZHONG et. al. emnlp 2020-11-12
487 Using GANs To Synthesise Minimum Training Data For Deepfake Generation
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Highlight: We observe that with such variability in facial expressions of synthetic GAN-generated training images and a reduced quantity of them, we can produce a near-realistic deepfake videos.		 Simranjeet Singh; Rajneesh Sharma; Alan F. Smeaton; arxiv-cs.CV 2020-11-10
488 A Cross-Verification Approach For Protecting World Leaders From Fake And Tampered Audio
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Highlight: We propose a subsequence alignment method based on the Needleman-Wunsch algorithm and show that it significantly outperforms dynamic time warping in handling common tampering operations.		 Mengyi Shan; TJ Tsai; arxiv-eess.AS 2020-10-23
489 Spatio-temporal Features For Generalized Detection Of Deepfake Videos
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Highlight: In this paper, we empirically show that existing approaches on image and sequence classifiers generalize poorly to new manipulation techniques.		 Ipek Ganiyusufoglu; L. Minh Ngô; Nedko Savov; Sezer Karaoglu; Theo Gevers; arxiv-cs.CV 2020-10-22
490 Towards Generalizable Deepfake Detection with Locality-aware AutoEncoder
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Highlight: Motivated by the fine-grained nature and spatial locality characteristics of deepfakes, we propose Locality-Aware AutoEncoder (LAE) to bridge the generalization gap.		 Mengnan Du; Shiva Pentyala; Yuening Li; Xia Hu; cikm 2020-10-19
491 DeHiDe: Deep Learning-based Hybrid Model To Detect Fake News Using Blockchain
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Highlight: This paper proposes a novel hybrid model DeHiDe: Deep Learning-based Hybrid Model to Detect Fake News using Blockchain.		 Prashansa Agrawal; Parwat Singh Anjana; Sathya Peri; arxiv-cs.LG 2020-10-17
492 DeepRhythm: Exposing DeepFakes With Attentional Visual Heartbeat Rhythms
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Highlight: In this work, we propose DeepRhythm, a DeepFake detection technique that exposes DeepFakes by monitoring the heartbeat rhythms.		 HUA QI et. al. mm 2020-10-12
493 FakePolisher: Making DeepFakes More Detection-Evasive By Shallow Reconstruction
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Highlight: Towards reducing the artifacts in the synthesized images, in this paper, we devise a simple yet powerful approach termed FakePolisher that performs shallow reconstruction of fake images through a learned linear dictionary, intending to effectively and efficiently reduce the artifacts introduced during image synthesis.		 YIHAO HUANG et. al. mm 2020-10-12
494 Emotions Don’t Lie: An Audio-Visual Deepfake Detection Method Using Affective Cues
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Highlight: We present a learning-based method for detecting real and fake deepfake multimedia content.		 Trisha Mittal; Uttaran Bhattacharya; Rohan Chandra; Aniket Bera; Dinesh Manocha; mm 2020-10-12
495 Sharp Multiple Instance Learning For DeepFake Video Detection
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Highlight: In this paper, we introduce a new problem of partial face attack in DeepFake video, where only video-level labels are provided but not all the faces in the fake videos are manipulated.		 XIAODAN LI et. al. mm 2020-10-12
496 DeepSonar: Towards Effective And Robust Detection Of AI-Synthesized Fake Voices
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Highlight: In this paper, we devise a novel approach, named DeepSonar, based on monitoring neuron behaviors of speaker recognition (SR) system, i.e., a deep neural network (DNN), to discern AI-synthesized fake voices.		 RUN WANG et. al. mm 2020-10-12
497 Feature Extraction Of Text For Deep Learning Algorithms: Application On Fake News Detection
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Highlight: In this research, it will be shown that by using deep learning algorithms and alphabet frequencies of the original text of a news without any information about the sequence of the alphabet can actually be used to classify fake news and trustworthy ones in high accuracy (85\%).		 HyeonJun Kim; arxiv-cs.CL 2020-10-12
498 Fake Metabolomics Chromatogram Generation for Facilitating Deep Learning of Peak-picking Neural Networks
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Abstract: Finding peaks in chromatograms and determining their start and end points (peak picking) is a core task in chromatography based biotechnology. Construction of peak-picking neural …		 SHINJI KANAZAWA et. al. Journal of bioscience and bioengineering 2020-10-10
499 DeepFakesON-Phys: DeepFakes Detection Based On Heart Rate Estimation
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Highlight: This work introduces a novel DeepFake detection framework based on physiological measurement.		 Javier Hernandez-Ortega; Ruben Tolosana; Julian Fierrez; Aythami Morales; arxiv-cs.CV 2020-10-01
500 FakeTagger: Robust Safeguards Against DeepFake Dissemination Via Provenance Tracking
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Highlight: In this paper, we investigate the potentials of image tagging in serving the DeepFake provenance tracking.		 Run Wang; Felix Juefei-Xu; Meng Luo; Yang Liu; Lina Wang; arxiv-cs.CR 2020-09-21
501 Dodging DeepFake Detection Via Implicit Spatial-Domain Notch Filtering
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Highlight: In this paper, we propose a simple yet powerful pipeline to reduce the artifact patterns of fake images without hurting image quality by performing implicit spatial-domain notch filtering.		 Yihao Huang; Felix Juefei-Xu; Qing Guo; Yang Liu; Geguang Pu; arxiv-cs.CV 2020-09-19
502 A Convolutional LSTM Based Residual Network For Deepfake Video Detection
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Highlight: In this paper, we addressed these limitations.		 Shahroz Tariq; Sangyup Lee; Simon S. Woo; arxiv-cs.CV 2020-09-16
503 Deep Detection For Face Manipulation
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Highlight: In this paper, we introduce a deep learning method to detect face manipulation.		 Disheng Feng; Xuequan Lu; Xufeng Lin; arxiv-cs.CV 2020-09-13
504 Deepfake Detection: Humans Vs. Machines
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Highlight: In this paper, we present a subjective study conducted in a crowdsourcing-like scenario, which systematically evaluates how hard it is for humans to see if the video is deepfake or not.		 Pavel Korshunov; Sébastien Marcel; arxiv-cs.CV 2020-09-07
505 Sentimental LIAR: Extended Corpus And Deep Learning Models For Fake Claim Classification
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Highlight: This paper aims to address this issue by proposing a novel deep learning approach for automated detection of false short-text claims on social media.		 Bibek Upadhayay; Vahid Behzadan; arxiv-cs.CL 2020-08-31
506 DeepFake Detection Based On The Discrepancy Between The Face And Its Context
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Highlight: We propose a method for detecting face swapping and other identity manipulations in single images.		 Yuval Nirkin; Lior Wolf; Yosi Keller; Tal Hassner; arxiv-cs.CV 2020-08-27
507 How Do The Hearts Of Deep Fakes Beat? Deep Fake Source Detection Via Interpreting Residuals With Biological Signals
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Highlight: Fake portrait video generation techniques have been posing a new threat to the society with photorealistic deep fakes for political propaganda, celebrity imitation, forged evidences, and other identity related manipulations.		 Umur Aybars Ciftci; Ilke Demir; Lijun Yin; arxiv-cs.CV 2020-08-25
508 Exposing Deep-faked Videos By Anomalous Co-motion Pattern Detection
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Highlight: In this paper, we propose an fully-interpretable video forensic method that is designed specifically to expose deep-faked videos.		 Gengxing Wang; Jiahuan Zhou; Ying Wu; arxiv-cs.CV 2020-08-11
509 Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data
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Highlight: Thus, we seek a proactive and sustainable solution on deepfake detection, that is agnostic to the evolution of generative models, by introducing artificial fingerprints into the models.		 Ning Yu; Vladislav Skripniuk; Sahar Abdelnabi; Mario Fritz; arxiv-cs.CR 2020-07-16
510 FakeCatcher: Detection Of Synthetic Portrait Videos Using Biological Signals
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Abstract: The recent proliferation of fake portrait videos poses direct threats on society, law, and privacy [1]. Believing the fake video of a politician, distributing fake pornographic …		 Umur Aybars Ciftci; Ilke Demir; Lijun Yin; IEEE transactions on pattern analysis and machine … 2020-07-15
511 Leveraging Frequency Analysis for Deep Fake Image Recognition
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Highlight: This paper addresses this shortcoming and our results reveal, that in frequency space, GAN-generated images exhibit severe artifacts that can be easily identified.		 JOEL FRANK et. al. icml 2020-07-11
512 Interpretable and Trustworthy Deepfake Detection Via Dynamic Prototypes
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Highlight: In this paper we propose a novel human-centered approach for detecting forgery in face images, using dynamic prototypes as a form of visual explanations.		 Loc Trinh; Michael Tsang; Sirisha Rambhatla; Yan Liu; arxiv-cs.CV 2020-06-27
513 Deepfake Detection Using Spatiotemporal Convolutional Networks
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Highlight: Better generative models and larger datasets have led to more realistic fake videos that can fool the human eye but produce temporal and spatial artifacts that deep learning approaches can detect. We created a benchmark of the performance of spatiotemporal convolutional methods using the Celeb-DF dataset.		 Oscar de Lima; Sean Franklin; Shreshtha Basu; Blake Karwoski; Annet George; arxiv-cs.CV 2020-06-25
514 The Eyes Know It: FakeET — An Eye-tracking Database To Understand Deepfake Perception
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Highlight: We present \textbf{FakeET}– an eye-tracking database to understand human visual perception of \emph{deepfake} videos.		 Parul Gupta; Komal Chugh; Abhinav Dhall; Ramanathan Subramanian; arxiv-cs.CV 2020-06-12
515 Defending Against GAN-based Deepfake Attacks Via Transformation-aware Adversarial Faces
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Highlight: This work aims to take an offensive measure to impede the generation of high-quality fake images or videos.		 Chaofei Yang; Lei Ding; Yiran Chen; Hai Li; arxiv-cs.CV 2020-06-12
516 The DeepFake Detection Challenge (DFDC) Dataset
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Highlight: To counter this emerging threat, we have constructed an extremely large face swap video dataset to enable the training of detection models, and organized the accompanying DeepFake Detection Challenge (DFDC) Kaggle competition.		 BRIAN DOLHANSKY et. al. arxiv-cs.CV 2020-06-12
517 Investigating The Impact Of Pre-processing And Prediction Aggregation On The DeepFake Detection Task
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Highlight: In this paper, we propose a pre-processing step to improve the training data quality and examine its effect on the performance of DeepFake detection.		 Polychronis Charitidis; Giorgos Kordopatis-Zilos; Symeon Papadopoulos; Ioannis Kompatsiaris; arxiv-cs.CV 2020-06-12
518 Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics
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Highlight: We present a new large-scale challenging DeepFake video dataset, Celeb-DF, which contains 5,639 high-quality DeepFake videos of celebrities generated using improved synthesis process.		 Yuezun Li; Xin Yang; Pu Sun; Honggang Qi; Siwei Lyu; cvpr 2020-06-08
519 Face X-Ray for More General Face Forgery Detection
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Highlight: In this paper we propose a novel image representation called face X-ray for detecting forgery in face images.		 LINGZHI LI et. al. cvpr 2020-06-08
520 Not Made for Each Other- Audio-Visual Dissonance-based Deepfake Detection and Localization
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Highlight: We propose detection of deepfake videos based on the dissimilarity between the audio and visual modalities, termed as the Modality Dissonance Score (MDS).		 Komal Chugh; Parul Gupta; Abhinav Dhall; Ramanathan Subramanian; arxiv-cs.CV 2020-05-29
521 DeepFake: Deep Dueling-based Deception Strategy to Defeat Reactive Jammers
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Highlight: In this paper, we introduce DeepFake, a novel deep reinforcement learning-based deception strategy to deal with reactive jamming attacks.		 Nguyen Van Huynh; Dinh Thai Hoang; Diep N. Nguyen; Eryk Dutkiewicz; arxiv-cs.NI 2020-05-12
522 Fake Face Detection Via Adaptive Residuals Extraction Network
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Highlight: We propose an adaptive residuals extraction network (AREN), which serves as pre-processing to suppress image content and highlight tampering artifacts.		 Zhiqing Guo; Gaobo Yang; Jiyou Chen; Xingming Sun; arxiv-cs.CV 2020-05-11
523 Deepfake Forensics Using Recurrent Neural Networks
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Highlight: This paper proposes a transient mindful pipeline to automat-ically recognize deepfake recordings.		 Rahul U; Ragul M; Raja Vignesh K; Tejeswinee K; arxiv-cs.CV 2020-05-01
524 Deepfake Video Forensics Based On Transfer Learning
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Highlight: Deepfake models can create fake images and videos that humans cannot differentiate them from the genuine ones.		 Rahul U; Ragul M; Raja Vignesh K; Tejeswinee K; arxiv-cs.CV 2020-04-29
525 Detecting Deep-Fake Videos From Appearance And Behavior
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Highlight: We describe a biometric-based forensic technique for detecting face-swap deep fakes.		 Shruti Agarwal; Tarek El-Gaaly; Hany Farid; Ser-Nam Lim; arxiv-cs.CV 2020-04-29
526 Preliminary Forensics Analysis Of DeepFake Images
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Highlight: This paper will present a brief overview of technologies able to produce DeepFake images of faces.		 Luca Guarnera; Oliver Giudice; Cristina Nastasi; Sebastiano Battiato; arxiv-cs.CV 2020-04-27
527 The Creation And Detection Of Deepfakes: A Survey
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Highlight: In this paper, we explore the creation and detection of deepfakes and provide an in-depth view of how these architectures work.		 Yisroel Mirsky; Wenke Lee; arxiv-cs.CV 2020-04-23
528 DeepFake Detection By Analyzing Convolutional Traces
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Highlight: In this work we focus on the analysis of Deepfakes of human faces with the objective of creating a new detection method able to detect a forensics trace hidden in images: a sort of fingerprint left in the image generation process.		 Luca Guarnera; Oliver Giudice; Sebastiano Battiato; arxiv-cs.CV 2020-04-22
529 DeepFakes Evolution: Analysis Of Facial Regions And Fake Detection Performance
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Highlight: This study provides an exhaustive analysis of both 1st and 2nd DeepFake generations in terms of facial regions and fake detection performance.		 Ruben Tolosana; Sergio Romero-Tapiador; Julian Fierrez; Ruben Vera-Rodriguez; arxiv-cs.CV 2020-04-16
530 Video Face Manipulation Detection Through Ensemble Of CNNs
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Highlight: In this paper, we tackle the problem of face manipulation detection in video sequences targeting modern facial manipulation techniques.		 NICOLÒ BONETTINI et. al. arxiv-cs.CV 2020-04-16
531 Deep Fakes And Memory Malleability: False Memories In The Service Of Fake News
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Abstract: Deep fakes have rapidly emerged as one of the most ominous concerns within modern society. The ability to easily and cheaply generate convincing images, audio, and video via …		 Nadine Liv; Dov Greenbaum; AJOB neuroscience 2020-04-02
532 Adversarial Perturbations Fool Deepfake Detectors
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Highlight: This work uses adversarial perturbations to enhance deepfake images and fool common deepfake detectors.		 Apurva Gandhi; Shomik Jain; arxiv-cs.CV 2020-03-23
533 Detecting Deepfakes With Metric Learning
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Highlight: In this work, we analyze several deep learning approaches in the context of deepfakes classification in high compression scenario and demonstrate that a proposed approach based on metric learning can be very effective in performing such a classification.		 Akash Kumar; Arnav Bhavsar; arxiv-cs.CV 2020-03-19
534 Leveraging Frequency Analysis For Deep Fake Image Recognition
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Highlight: In this paper, we address this shortcoming and our results reveal that in frequency space, GAN-generated images exhibit severe artifacts that can be easily identified.		 JOEL FRANK et. al. arxiv-cs.CV 2020-03-19
535 A Novel Counterfeit Feature Extraction Technique For Exposing Face-Swap Images Based On Deep Learning And Error Level Analysis
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Abstract: The quality and efficiency of generating face-swap images have been markedly strengthened by deep learning. For instance, the face-swap manipulations by DeepFake are so real that …		 Weiguo Zhang; Chenggang Zhao; Yuxing Li; Entropy (Basel, Switzerland) 2020-02-21
536 Amplifying The Uncanny
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Abstract: Deep neural networks have become remarkably good at producing realistic deepfakes, images of people that (to the untrained eye) are indistinguishable from real images. Deepfakes …		 Terence Broad; Frederic Fol Leymarie; Mick Grierson; arxiv-cs.CV 2020-02-17
537 Adversarial Deepfakes: Evaluating Vulnerability Of Deepfake Detectors To Adversarial Examples
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Highlight: In this work, we demonstrate that it is possible to bypass such detectors by adversarially modifying fake videos synthesized using existing Deepfake generation methods.		 Shehzeen Hussain; Paarth Neekhara; Malhar Jere; Farinaz Koushanfar; Julian McAuley; arxiv-cs.CV 2020-02-09
538 Call For Special Issue Papers: The Social Impact Of Deep Fakes
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 Jeremy Bailenson; Jeff Hancock; Cyberpsychology, behavior and social networking 2020-02-08
539 FakeLocator: Robust Localization of GAN-Based Face Manipulations
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Highlight: In this work, we investigate the architecture of existing GAN-based face manipulation methods and observe that the imperfection of upsampling methods therewithin could be served as an important asset for GAN-synthesized fake image detection and forgery localization.		 Yihao Huang; Felix Juefei-Xu; Qing Guo; Yang Liu; Geguang Pu; arxiv-cs.CV 2020-01-27
540 FDFtNet: Facing Off Fake Images Using Fake Detection Fine-tuning Network
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Highlight: In this work, we propose a light-weight robust fine-tuning neural network-based classifier architecture called Fake Detection Fine-tuning Network (FDFtNet), which is capable of detecting many of the new fake face image generation models, and can be easily combined with existing image classification networks and finetuned on a few datasets.		 Hyeonseong Jeon; Youngoh Bang; Simon S. Woo; arxiv-cs.CV 2020-01-05
541 DeepFake Face Image Detection Based on Improved VGG Convolutional Neural Network
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Abstract: DeepFake can forge high-quality tampered images and videos that are consistent with the distribution of real data. Its rapid development causes people’s panic and reflection. In …		 Xu Chang; Jian Wu; Tongfeng Yang; Guorui Feng; 2020 39th Chinese Control Conference (CCC) 2020-01-01
542 Whose Dystopia Is It Anyway? Deepfakes and Social Media Regulation
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Abstract: This study explores global journalistic discussions of deepfake applications (audiovisual manipulating applications based on artificial intelligence (AI)) to understand the …		 Aya Yadlin-Segal; Yael Oppenheim; Convergence: The International Journal of Research into New … 2020-01-01
543 Deepfake:
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Abstract: El deepfake es una especie mediática que evoluciona progresivamente como una amenaza para los dicursos del hacer creer en tiempos de la posverdad. Este estudio tiene como objetivo …		 Jacob Bañuelos Capistrán; Revista Panamericana de Comunicación 2020-01-01
544 The Problem of Deepfake Videos and How to Counteract Them in Smart Cities
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 Samuel McCammon; Claudia Tweedie; Kevin Curran; Security and Organization within IoT and Smart Cities 2020-01-01
545 Deepfake: New Era in The Age of Disinformation & End of Reliable Journalism
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Abstract: Haberciligin var oldugu ilk andan itibaren sahte haberlerin ve dolayisiyla guvenilir habercilik probleminin var oldugunu soylemek mumkundur. Ancak son yuzyilda yasanan hizli …		 Erkam Temir; 2020-01-01
546 Information Reliability: Criteria to Identify Misinformation in The Digital Environment
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Abstract: The article presents information reliability criteria to identify misinformation and its representations (fake news, post truth, alternative facts and deepfake) in the current …		 Leonardo Ripoll; José Claudio Matos; 2020-01-01
547 AI-synthesized Avatars: from Real-time Deepfakes to Virtual AI Companions
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Abstract: We introduce a real-time deep learning-based face synthesis technology for photoreal AI-avatars, and demonstrate two novel applications. We showcase the first zero-shot real-time …		 ZEJIAN WANG et. al. ACM SIGGRAPH 2020 Real-Time Live! 2020-01-01
548 Emergence and Functionality of 3D Videos
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Abstract: Millions of videos are created and watched every day. Quality of desired video plays a significant role in modern day applications. Deep fake videos have alarmed the established …		 International Journal of Engineering and Advanced Technology 2020-01-01
549 A Study on Combating Emerging Threat of Deepfake Weaponization
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Abstract: A breakthrough in the emerging use of machine learning and deep learning is the concept of autoencoders and GAN (Generative Adversarial Networks), architectures that can generate …		 Rahul Katarya; Anushka Lal; 2020 Fourth International Conference on I-SMAC (IoT in … 2020-01-01
550 Integrating Deepfake Detection Into Cybersecurity Curriculum
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Abstract: Deep Learning (DL) has been widely adopted in the domain of cybersecurity to address a variety of security and privacy concerns. Moreover, in recent years attackers are also …		 Hongmei Chi; Udochi Maduakor; Richard A. Alo; Eleason A. Williams; 2020-01-01
551 Deep Fake Image Detection Based on Pairwise Learning
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Abstract: Generative adversarial networks (GANs) can be used to generate a photo-realistic image from a low-dimension random noise. Such a synthesized (fake) image with inappropriate …		 Chih-Chung Hsu; Yi-Xiu Zhuang; Chia-Yen Lee; Applied Sciences 2020-01-01
552 LEGAL ASPECTS OF DEEPFAKES
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Abstract: The purpose of this article is to bring closer the legal aspects of deepfake. Beginning with what exactly deepfake is and what its history is, in what areas it is and can be used …		 Maciej Langer; Jakub Wyczik; Roczniki Administracji i Prawa 2020-01-01
553 Deep Learning in Face Synthesis: A Survey on Deepfakes
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Abstract: Deepfake stemming from the combined words of deep learning and fake, refers to a type of fake images and video generation technology based on artificial intelligence. In …		 Teng Zhang; Lirui Deng; Liang Zhang; Xianglei Dang; 2020 IEEE 3rd International Conference on Computer and … 2020-01-01
554 A System for Mitigating The Problem of Deepfake News Videos Using Watermarking
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Abstract: This paper describes how watermarking technology can be used to prevent the proliferation of Deepfake news. In the proposed system, digital watermarks are embedded in the audio …		 Adnan M. Alattar; Ravi K. Sharma; John Scriven; Electronic Imaging 2020-01-01
555 Fontcuberta, Humorista Y Demiurgo: Mundos Alternativos Perfectamente Amueblados (MAPA)
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Abstract: In the era of post-truth politics, fake news and deepfake (specific digital counterfeit of visual documents) it becomes more necessary than ever to claim the commitment to the …		 Raúl Rodríguez Ferrándiz; Arte Individuo Y Sociedad 2020-01-01
556 Deepfakes: Temporal Sequential Analysis to Detect Face-swapped Video Clips Using Convolutional Long Short-term Memory
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Abstract: Abstract. Deepfake (a bag of “deep learning” and “fake”) is a technique for human image synthesis based on artificial intelligence, i.e., to superimpose the existing (source) …		 Sawinder Kaur; Parteek Kumar; Ponnurangam Kumaraguru; Journal of Electronic Imaging 2020-01-01
557 Fake Video Detection With Certainty-Based Attention Network
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Abstract: DeepFake synthesizes realistic fake videos that could be used maliciously such as manipulation and harassment. In order to prevent such malicious usages, detecting fake videos is …		 Dae Hwi Choi; Hong Joo Lee; Sang Min Lee; Jung Uk Kim; Yong Man Ro; 2020 IEEE International Conference on Image Processing … 2020-01-01
558 Deep Learning Model-based Multimedia Forgery Detection
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Abstract: Images and videos can be spread very conveniently using social media platforms like WhatsApp and Facebook. The authenticity of this information cannot be verified easily but it …		 Yash Shah; Parth Shah; Mansi Patel; Chinmay Khamkar; Pratik Kanani; 2020 Fourth International Conference on I-SMAC (IoT in … 2020-01-01
559 Deepfakes: Trick or Treat?
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Abstract: Although manipulations of visual and auditory media are as old as the media themselves, the recent entrance of deepfakes has marked a turning point in the creation of fake …		 Jan Kietzmann; Linda W. Lee; Ian P. McCarthy; Tim C. Kietzmann; Business Horizons 2020-01-01
560 MMD Based Discriminative Learning for Face Forgery Detection
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Abstract: Face forensic detection is to distinguish manipulated from pristine face images. The main drawback of existing face forensics detection methods is their limited generalization …		 Jian Han; Theo Gevers; 2020-01-01
561 O Contexto Informacional Contemporâneo: O Crescimento Da Desinformação E Suas Manifestações No Ambiente Digital
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Abstract: Introducao : O artigo aborda o atual cenario da desinformacao presente na web com o surgimento de terminologias como ‘ fake news ’, ‘ pos-verdade ’, ‘ deepfake ’, entre outros. …		 Leonardo Ripoll; José Claudio Matos; Information Professional 2020-01-01
562 GAN and DRL Based Intent Translation and Deep Fake Configuration Generation for Optimization
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Abstract: The requirements of next-generation of networks have imposed a multi-dimensional/ directional complexity over network management. As a result, the only way forward is through the …		 Talha Ahmed Khan; Khizar Abbas; Muhammad Afaq; Adeel Rafiq; Wang-Cheol Song; 2020 International Conference on Information and … 2020-01-01
563 Adversarial Attack on Deepfake Detection Using RL Based Texture Patches
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 Steven Lawrence Fernandes; Sumit Kumar Jha; 2020-01-01
564 Generative Adversarial Ensemble Learning for Face Forensics
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Abstract: The recent advance of synthetic image generation and manipulation methods allows us to generate synthetic face images close to real images. On the other hand, the importance of …		 Jae-Yong Baek; Yong-Sang Yoo; Seung-Hwan Bae; IEEE Access 2020-01-01
565 A Comparative Study of Deepfake Video Detection Method
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Abstract: Deepfake technology allows humans to manipulate images and videos using deep learning technology. The results from deepfakes are very difficult to distinguish using ordinary …		 Kurniawan Nur Ramadhani; Rinaldi Munir; 2020 3rd International Conference on Information and … 2020-01-01
566 Deepfake Video Detection Using Convolutional Neural Network
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 Aarti Karandikar; 2020-01-01
567 Seeing Is Deceiving: The Psychology and Neuroscience of Fake Faces
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Abstract: Face perception is an essential skill for human interaction and social learning, impacting how we build relationships and perceive the world around us. In the modern era, face …		 S Manohar; 2020-01-01
568 Detection of Deepfakes Using Visual Artifacts and Neural Network Classifier
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Abstract: The field of Artificial Intelligence is so advanced that it made the creation and modification of synthetic images and videos very easy. Tampering of videos attained a new level …		 M. A. Sahla Habeeba; A. Lijiya; Anu Mary Chacko; 2020-01-01
569 OC-FakeDect: Classifying Deepfakes Using One-class Variational Autoencoder
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Abstract: An image forgery method called Deepfakes can cause security and privacy issues by changing the identity of a person in a photo through the replacement of his/her face with a …		 Hasam Khalid; Simon S. Woo; 2020 IEEE/CVF Conference on Computer Vision and Pattern … 2020-01-01
570 Detecting Deep-Fake Videos from Phoneme-Viseme Mismatches
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Abstract: Recent advances in machine learning and computer graphics have made it easier to convincingly manipulate video and audio. These so-called deep-fake videos range from complete …		 Shruti Agarwal; Hany Farid; Ohad Fried; Maneesh Agrawala; 2020 IEEE/CVF Conference on Computer Vision and Pattern … 2020-01-01
571 Relatório Técnico Conclusivo – Inteligência Artificial, Liberdade De Expressão E Deep Fake News – Sem 1
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 Fabiano Hartmann Peixoto; Eduardo Dib Maximiniano Junqueira; 2020-01-01
572 Methods of Deepfake Detection Based on Machine Learning
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Abstract: Nowadays, people faced an emerging problem of AI-synthesized face swapping videos, widely known as the DeepFakes. This kind of videos can be created to cause threats to privacy, …		 Artem A. Maksutov; Viacheslav O. Morozov; Aleksander A. Lavrenov; Alexander S. Smirnov; 2020 IEEE Conference of Russian Young Researchers in … 2020-01-01
573 The Eyes Know It: FakeET- An Eye-tracking Database to Understand Deepfake Perception
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Abstract: We present FakeET — an eye-tracking database to understand human visual perception of deepfake videos. Given that the principal purpose of deepfakes is to deceive human …		 Parul Gupta; Komal Chugh; Abhinav Dhall; Ramanathan Subramanian; Proceedings of the 2020 International Conference on … 2020-01-01
574 SDHF: Spotting DeepFakes with Hierarchical Features
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Abstract: DeepFake videos are widely distributed on social media platforms, which has seriously affected the authenticity of digital media content, calling for robust DeepFake detection …		 TAO LIANG et. al. 2020 IEEE 32nd International Conference on Tools with … 2020-01-01
575 CRIME IN THE TIME OF THE PLAGUE: FAKE NEWS PANDEMIC AND THE CHALLENGES TO LAW-ENFORCEMENT AND INTELLIGENCE COMMUNITY
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Abstract: The Paper explores the problem of fake news and disinformation campaigns in the turmoil era of the COVID-19 coronavirus pandemic. The Author addresses the problem from the …		 Kacper Gradoń; Society Register 2020-01-01
576 Effective and Fast DeepFake Detection Method Based on Haar Wavelet Transform
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Abstract: DeepFake using Generative Adversarial Networks (GANs) tampered videos reveals a new challenge in today’s life. With the inception of GANs, generating high-quality fake videos …		 Mohammed Akram Younus; Taha Mohammed Hasan; 2020 International Conference on Computer Science and … 2020-01-01
577 Will Deepfakes Do Deep Damage?
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Abstract: The ability to produce fake videos that appear amazingly real is here. Researchers are now developing ways to detect and prevent them. …		 Samuel Greengard; Communications of the ACM 2020-01-01
578 Detecting Deepfakes Using Crowd Consensus
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Abstract: Deepfake videos are becoming more and more believable, outpacing fake detection methods. As a research community, we must embrace a wider variety of detection tactics to ke