Paper Digest: Recent Papers on Deep Fake
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 | NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Media Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce several strategies for automatic retrieval of suitable images for the given captions, capturing cases with related semantics but inconsistent entities as well as matching entities but inconsistent semantic context. While some prior datasets for detecting image-text inconsistency can be solved with blind models due to linguistic cues introduced by text manipulation, we propose a dataset where both image and text are unmanipulated but mismatched. |
Grace Luo; Trevor Darrell; Anna Rohrbach; | arxiv-cs.CV | 2021-04-12 |
| 2 | Generalized Spoofing Detection Inspired from Audio Generation Artifacts Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, we propose a novel use of long-range spectro-temporal modulation feature — 2D DCT over log-Mel spectrogram for the audio deepfake detection. |
Yang Gao; Tyler Vuong; Mahsa Elyasi; Gaurav Bharaj; Rita Singh; | arxiv-cs.SD | 2021-04-08 |
| 3 | Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper reports the first successful application of a differentiable architecture search (DARTS) approach to the deepfake and spoofing detection problems. |
Wanying Ge; Michele Panariello; Jose Patino; Massimiliano Todisco; Nicholas Evans; | arxiv-cs.LG | 2021-04-07 |
| 4 | Towards Measuring Fairness in AI: The Casual Conversations Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces a novel dataset to help researchers evaluate their computer vision and audio models for accuracy across a diverse set of age, genders, apparent skin tones and ambient lighting conditions. |
CANER HAZIRBAS et. al. | arxiv-cs.CV | 2021-04-06 |
| 5 | Deepfake Detection Scheme Based on Vision Transformer and Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 6 | Deepfake Forensics Via An Adversarial Game Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we advocate adversarial training for improving the generalization ability to both unseen facial forgeries and unseen image/video qualities. |
Zhi Wang; Yiwen Guo; Wangmeng Zuo; | arxiv-cs.CV | 2021-03-24 |
| 7 | The Real Threat of Deepfake Pornography: A Review of Canadian Policy Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 8 | Deepfakes: Awareness, Concerns, and Platform Accountability Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: A 61 question survey was used to examine issues around deepfake technology. In total, 319 respondents answered questions around awareness, concerns, and the responsibility of … |
Justin D Cochran; Stuart A Napshin; | Cyberpsychology, behavior and social networking | 2021-03-24 |
| 9 | KoDF: A Large-scale Korean DeepFake Detection Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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. Facing the emerging threat of deepfakes, we have built the Korean DeepFake Detection Dataset (KoDF), a large-scale collection of synthesized and real videos focused on Korean subjects. |
Patrick Kwon; Jaeseong You; Gyuhyeon Nam; Sungwoo Park; Gyeongsu Chae; | arxiv-cs.CV | 2021-03-18 |
| 10 | DefakeHop: A Light-Weight High-Performance Deepfake Detector Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 11 | An Introduction to Deep Generative Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 12 | Deepfake Videos in The Wild: Analysis and Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Even if detection schemes are shown to perform well on existing datasets, it is unclear how well the methods generalize to real-world deepfakes. 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. |
JIAMENG PU et. al. | arxiv-cs.CR | 2021-03-06 |
| 13 | Multi-attentional Deepfake Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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. | arxiv-cs.CV | 2021-03-03 |
| 14 | DeepFake-o-meter: An Open Platform for DeepFake Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 15 | Effects of Disinformation Using Deepfake: The Protective Effect of Media Literacy Education Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 16 | I Found A More Attractive Deepfaked Self: The Self-Enhancement Effect in Deepfake Video Exposure Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: With the introduction of deepfake technology, which enables digital face-swapping between two individuals, young women are no longer passive viewers of attractive celebrities, but … |
Fuzhong Wu; Yueran Ma; Zheng Zhang; | Cyberpsychology, behavior and social networking | 2021-03-01 |
| 17 | Am I A Real or Fake Celebrity? Measuring Commercial Face Recognition Web APIs Under Deepfake Impersonation Attack Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 18 | Countering Malicious DeepFakes: Survey, Battleground, and Horizon Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To fill this gap, in this paper, we provide a comprehensive overview and detailed analysis of the research work on the topic of DeepFake generation, DeepFake detection as well as evasion of DeepFake detection, with more than 191 research papers carefully surveyed. |
FELIX JUEFEI-XU et. al. | arxiv-cs.CV | 2021-02-27 |
| 19 | Deepfakes Unmasked: The Effects of Information Priming and Bullshit Receptivity on Deepfake Recognition and Sharing Intention Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 20 | Deepfake Video Detection Using Convolutional Vision Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 21 | Improving DeepFake Detection Using Dynamic Face Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we provide a quantitative analysis to investigate this problem and present a solution to prevent model overfitting due to the high volume of samples generated from a small number of actors. |
Sowmen Das; Arup Datta; Md. Saiful Islam; Md. Ruhul Amin; | arxiv-cs.CV | 2021-02-18 |
| 22 | Popular Discourse Around Deepfakes and The Interdisciplinary Challenge of Fake Video Distribution Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 23 | To Believe or Not to Believe: Framing Analysis of Content and Audience Response of Top 10 Deepfake Videos on YouTube Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 24 | Adversarially Robust Deepfake Media Detection Using Fused Convolutional Neural Network Predictions Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 25 | The Deepfake Detection Dilemma: A Multistakeholder Exploration of Adversarial Dynamics in Synthetic Media Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper describes how a multistakeholder cohort from academia, technology platforms, media entities, and civil society organizations active in synthetic media detection and its socio-technical implications evaluates the detection dilemma. |
Claire Leibowicz; Sean McGregor; Aviv Ovadya; | arxiv-cs.CY | 2021-02-11 |
| 26 | Landmark Breaker: Obstructing DeepFake By Disturbing Landmark Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 27 | Detecting Deepfake Videos Using Euler Video Magnification IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 28 | Fighting Deepfakes By Detecting GAN DCT Anomalies IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, a new fast detection method able to discriminate Deepfake images with high precision is proposed. |
Oliver Giudice; Luca Guarnera; Sebastiano Battiato; | arxiv-cs.CV | 2021-01-24 |
| 29 | BERT Transformer Model for Detecting Arabic GPT2 Auto-Generated Tweets IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 30 | FakeBuster: A DeepFakes Detection Tool for Video Conferencing Scenarios IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 31 | Exploring Adversarial Fake Images on Face Manifold IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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; | arxiv-cs.CV | 2021-01-08 |
| 32 | Data Poisoning Attacks to Deep Learning Based Recommender Systems IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 33 | FakeBERT: Fake News Detection in Social Media with A BERT-based Deep Learning Approach IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 34 | WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 35 | Where Do Deep Fakes Look? Synthetic Face Detection Via Gaze Tracking IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We evaluate our approach on several deep fake datasets, achieving 89.79\% accuracy on FaceForensics++, 80.0\% on Deep Fakes (in the wild), and 88.35\% on CelebDF datasets. |
Ilke Demir; Umur A. Ciftci; | arxiv-cs.CV | 2021-01-04 |
| 36 | EchoFakeD: Improving Fake News Detection in Social Media with An Efficient Deep Neural Network IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 37 | Identifying Invariant Texture Violation For Robust Deepfake Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 38 | Learning To Recognize Patch-Wise Consistency For Deepfake Detection IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose to detect Deepfake generated by face manipulation based on one of their fundamental features: images are blended by patches from multiple sources, carrying distinct and persistent source features. |
TIANCHEN ZHAO et. al. | arxiv-cs.CV | 2020-12-16 |
| 39 | Interdisciplinary Lessons Learned While Researching Fake News IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 40 | Responsible Disclosure of Generative Models Using Scalable Fingerprinting IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Over the past six years, deep generative models have achieved a qualitatively new level of performance. |
Ning Yu; Vladislav Skripniuk; Dingfan Chen; Larry Davis; Mario Fritz; | arxiv-cs.CR | 2020-12-15 |
| 41 | The Emerging Threats Of Deepfake Attacks And Countermeasures IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 42 | Identity-Driven DeepFake Detection IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present an alternative approach: Identity-Driven DeepFake Detection. |
XIAOYI DONG et. al. | arxiv-cs.CV | 2020-12-07 |
| 43 | Cost Sensitive Optimization Of Deepfake Detector IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 44 | Technology-driven Alteration Of Nonverbal Cues And Its Effects On Negotiation IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 45 | ID-Reveal: Identity-aware DeepFake Video Detection IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we look at the problem from a different perspective by focusing on the facial characteristics of a specific identity; i.e., we want to answer the question ‘Is this the person who is claimed to be?’ |
Davide Cozzolino; Andreas Rössler; Justus Thies; Matthias Nießner; Luisa Verdoliva; | arxiv-cs.CV | 2020-12-04 |
| 46 | Adversarial Threats To DeepFake Detection: A Practical Perspective IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 47 | AOT: Appearance Optimal Transport Based Identity Swapping For Forgery Detection IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we provide a new identity swapping algorithm with large differences in appearance for face forgery detection. |
HAO ZHU et. al. | nips | 2020-11-17 |
| 48 | Training Strategies And Data Augmentations In CNN-based DeepFake Video Detection IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 49 | Neural Deepfake Detection With Factual Structure Of Text IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 50 | Using GANs To Synthesise Minimum Training Data For Deepfake Generation IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 51 | A Cross-Verification Approach For Protecting World Leaders From Fake And Tampered Audio IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 52 | Spatio-temporal Features For Generalized Detection Of Deepfake Videos IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 53 | Towards Generalizable Deepfake Detection With Locality-aware AutoEncoder IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 54 | DeHiDe: Deep Learning-based Hybrid Model To Detect Fake News Using Blockchain IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 55 | DeepRhythm: Exposing DeepFakes With Attentional Visual Heartbeat Rhythms IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 56 | FakePolisher: Making DeepFakes More Detection-Evasive By Shallow Reconstruction IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 57 | Sharp Multiple Instance Learning For DeepFake Video Detection IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 58 | Emotions Don’t Lie: An Audio-Visual Deepfake Detection Method Using Affective Cues IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 59 | DeepSonar: Towards Effective And Robust Detection Of AI-Synthesized Fake Voices IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 60 | Feature Extraction Of Text For Deep Learning Algorithms: Application On Fake News Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 61 | Fake Metabolomics Chromatogram Generation for Facilitating Deep Learning of Peak-picking Neural Networks IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 62 | DeepFakesON-Phys: DeepFakes Detection Based On Heart Rate Estimation IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 63 | DeepTag: Robust Image Tagging For DeepFake Provenance IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigated the potentials of image tagging in serving the DeepFake provenance. |
RUN WANG et. al. | arxiv-cs.CR | 2020-09-21 |
| 64 | FakeRetouch: Evading DeepFakes Detection Via The Guidance Of Deliberate Noise IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to further improve the fidelity of DeepFake images, in this work, we propose a simple yet powerful framework to reduce the artifact patterns of fake images without hurting image quality. |
YIHAO HUANG et. al. | arxiv-cs.CV | 2020-09-19 |
| 65 | A Convolutional LSTM Based Residual Network For Deepfake Video Detection IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we addressed these limitations. |
Shahroz Tariq; Sangyup Lee; Simon S. Woo; | arxiv-cs.CV | 2020-09-16 |
| 66 | Deep Detection For Face Manipulation IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 67 | Deepfake Detection: Humans Vs. Machines IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 68 | Detection Of AI-Synthesized Speech Using Cepstral & Bispectral Statistics IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an approach to distinguish human speech from AI synthesized speech exploiting the Bi-spectral and Cepstral analysis. |
Arun K. Singh; Priyanka Singh; | arxiv-cs.LG | 2020-09-03 |
| 69 | Sentimental LIAR: Extended Corpus And Deep Learning Models For Fake Claim Classification IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 70 | DeepFake Detection Based On The Discrepancy Between The Face And Its Context IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 71 | Privacy Intelligence: A Survey On Image Sharing On Online Social Networks IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we present a high-level analysis framework based on the entire lifecycle of OSN image sharing to address the various privacy issues and solutions facing this interdisciplinary field. |
Chi Liu; Tianqing Zhu; Jun Zhang; Wanlei Zhou; | arxiv-cs.CR | 2020-08-27 |
| 72 | How Do The Hearts Of Deep Fakes Beat? Deep Fake Source Detection Via Interpreting Residuals With Biological Signals IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 73 | On Attribution of Deepfakes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We evaluate our method on the seminal example of face synthesis, demonstrating that our approach achieves 97.62% attribution accuracy, and is less sensitive to perturbations and adversarial examples. |
Baiwu Zhang; Jin Peng Zhou; Ilia Shumailov; Nicolas Papernot; | arxiv-cs.LG | 2020-08-20 |
| 74 | Exposing Deep-faked Videos By Anomalous Co-motion Pattern Detection IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 75 | Two-branch Recurrent Network For Isolating Deepfakes In Videos IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a method for deepfake detection based on a two-branch network structure that isolates digitally manipulated faces by learning to amplify artifacts while suppressing the high-level face content. |
Iacopo Masi; Aditya Killekar; Royston Marian Mascarenhas; Shenoy Pratik Gurudatt; Wael AbdAlmageed; | arxiv-cs.CV | 2020-08-07 |
| 76 | TweepFake: About Detecting Deepfake Tweets IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To help the research in this field, we collected a dataset of real Deepfake tweets. |
Tiziano Fagni; Fabrizio Falchi; Margherita Gambini; Antonio Martella; Maurizio Tesconi; | arxiv-cs.CL | 2020-07-31 |
| 77 | End-to-End Signal Factorization For Speech: Identity, Content, And Style IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on these findings, a new technique is proposed to factorize multiple types of information from the speech signal simultaneously using a combination of state-of-the-art machine learning methods for speech processing. |
Jennifer Williams; | ijcai | 2020-07-21 |
| 78 | Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 79 | FakeCatcher: Detection Of Synthetic Portrait Videos Using Biological Signals IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 80 | Leveraging Frequency Analysis For Deep Fake Image Recognition IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 81 | Interpretable and Trustworthy Deepfake Detection Via Dynamic Prototypes IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 82 | Deepfake Detection Using Spatiotemporal Convolutional Networks IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 83 | OGAN: Disrupting Deepfakes With An Adversarial Attack That Survives Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose the Oscillating GAN (OGAN) attack, a novel attack optimized to be training-resistant, which introduces spatial-temporal distortions to the output of face-swapping autoencoders. To implement OGAN, we construct a bilevel optimization problem, where we train a generator and a face-swapping model instance against each other. |
Eran Segalis; Eran Galili; | arxiv-cs.CV | 2020-06-17 |
| 84 | Investigating The Impact Of Pre-processing And Prediction Aggregation On The DeepFake Detection Task IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 85 | Defending Against GAN-based Deepfake Attacks Via Transformation-aware Adversarial Faces IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 86 | The DeepFake Detection Challenge (DFDC) Dataset IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 87 | The Eyes Know It: FakeET — An Eye-tracking Database To Understand Deepfake Perception Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 88 | Protecting Against Image Translation Deepfakes By Leaking Universal Perturbations From Black-Box Neural Networks IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we develop efficient disruptions of black-box image translation deepfake generation systems. |
Nataniel Ruiz; Sarah Adel Bargal; Stan Sclaroff; | arxiv-cs.CV | 2020-06-11 |
| 89 | A Note On Deepfake Detection With Low-Resources IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we present two methods that allow detecting Deepfakes for a user without significant computational power. |
Piotr Kawa; Piotr Syga; | arxiv-cs.CV | 2020-06-09 |
| 90 | Face X-Ray For More General Face Forgery Detection IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 91 | Watch Your Up-Convolution: CNN Based Generative Deep Neural Networks Are Failing To Reproduce Spectral Distributions IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we show that common up-sampling methods, i.e. known as up-convolution or transposed convolution, are causing the inability of such models to reproduce spectral distributions of natural training data correctly. |
Ricard Durall; Margret Keuper; Janis Keuper; | cvpr | 2020-06-08 |
| 92 | Celeb-DF: A Large-Scale Challenging Dataset For DeepFake Forensics IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 93 | Advancing High Fidelity Identity Swapping For Forgery Detection IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we study various existing benchmarks for deepfake detection researches. |
Lingzhi Li; Jianmin Bao; Hao Yang; Dong Chen; Fang Wen; | cvpr | 2020-06-08 |
| 94 | CNN-Generated Images Are Surprisingly Easy To Spot… For Now Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we ask whether it is possible to create a “universal” detector for telling apart real images from these generated by a CNN, regardless of architecture or dataset used. |
Sheng-Yu Wang; Oliver Wang; Richard Zhang; Andrew Owens; Alexei A. Efros; | cvpr | 2020-06-08 |
| 95 | Not Made for Each Other- Audio-Visual Dissonance-based Deepfake Detection and Localization IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 96 | DeepFaceLab: A Simple, Flexible And Extensible Face Swapping Framework IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we detail the principles that drive the implementation of DeepFaceLab and introduce the pipeline of it, through which every aspect of the pipeline can be modified painlessly by users to achieve their customization purpose, and it’s noteworthy that DeepFaceLab could achieve results with high fidelity and indeed indiscernible by mainstream forgery detection approaches. |
IVAN PEROV et. al. | arxiv-cs.CV | 2020-05-11 |
| 97 | Fake Face Detection Via Adaptive Residuals Extraction Network IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 98 | Deepfake Forensics Using Recurrent Neural Networks IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 99 | Deepfake Video Forensics Based On Transfer Learning IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 100 | Detecting Deep-Fake Videos From Appearance And Behavior IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 101 | Preliminary Forensics Analysis Of DeepFake Images IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 102 | Deepfakes Detection With Automatic Face Weighting IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a method based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) that extracts visual and temporal features from faces present in videos to accurately detect manipulations. |
DANIEL MAS MONTSERRAT et. al. | arxiv-cs.CV | 2020-04-24 |
| 103 | The Creation And Detection Of Deepfakes: A Survey IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 104 | DeepFake Detection By Analyzing Convolutional Traces IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 105 | Video Face Manipulation Detection Through Ensemble Of CNNs IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 106 | DeepFakes Evolution: Analysis Of Facial Regions And Fake Detection Performance IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 107 | Deep Fakes And Memory Malleability: False Memories In The Service Of Fake News IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 108 | Evading Deepfake-Image Detectors With White- And Black-Box Attacks IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We show that such forensic classifiers are vulnerable to a range of attacks that reduce the classifier to near-0% accuracy. |
Nicholas Carlini; Hany Farid; | arxiv-cs.CV | 2020-04-01 |
| 109 | Adversarial Perturbations Fool Deepfake Detectors IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 110 | Leveraging Frequency Analysis For Deep Fake Image Recognition IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 111 | Detecting Deepfakes With Metric Learning IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 112 | Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks And Facial Manipulation Systems IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a spread-spectrum adversarial attack, which evades blur defenses. |
Nataniel Ruiz; Sarah Adel Bargal; Stan Sclaroff; | arxiv-cs.CV | 2020-03-02 |
| 113 | A Novel Counterfeit Feature Extraction Technique For Exposing Face-Swap Images Based On Deep Learning And Error Level Analysis IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 114 | Amplifying The Uncanny IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 115 | Adversarial Deepfakes: Evaluating Vulnerability Of Deepfake Detectors To Adversarial Examples IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 116 | Call For Special Issue Papers: The Social Impact Of Deep Fakes IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details |
Jeremy Bailenson; Jeff Hancock; | Cyberpsychology, behavior and social networking | 2020-02-08 |
| 117 | Media Forensics And DeepFakes: An Overview IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This review paper aims to present an analysis of the methods for visual media integrity verification, that is, the detection of manipulated images and videos. |
Luisa Verdoliva; | arxiv-cs.CV | 2020-01-17 |
| 118 | Advbox: A Toolbox To Generate Adversarial Examples That Fool Neural Networks IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In recent years, neural networks have been extensively deployed for computer vision tasks, particularly visual classification problems, where new algorithms reported to achieve or even surpass the human performance. |
DOU GOODMAN et. al. | arxiv-cs.LG | 2020-01-13 |
| 119 | FDFtNet: Facing Off Fake Images Using Fake Detection Fine-tuning Network IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details 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 |
| 120 | DeepFakes And Beyond: A Survey Of Face Manipulation And Fake Detection IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Among all the aspects discussed in the survey, we pay special attention to the latest generation of DeepFakes, highlighting its improvements and challenges for fake detection. |
Ruben Tolosana; Ruben Vera-Rodriguez; Julian Fierrez; Aythami Morales; Javier Ortega-Garcia; | arxiv-cs.CV | 2020-01-01 |
| 121 | Unmasking DeepFakes With Simple Features IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present a simple way to detect such fake face images – so-called DeepFakes. |
Ricard Durall; Margret Keuper; Franz-Josef Pfreundt; Janis Keuper; | arxiv-cs.LG | 2019-11-02 |
| 122 | Use Of A Capsule Network To Detect Fake Images And Videos IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a capsule network that can detect various kinds of attacks, from presentation attacks using printed images and replayed videos to attacks using fake videos created using deep learning. |
Huy H. Nguyen; Junichi Yamagishi; Isao Echizen; | arxiv-cs.CV | 2019-10-28 |
| 123 | FaceForensics++: Learning To Detect Manipulated Facial Images IF:5 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To standardize the evaluation of detection methods, we propose an automated benchmark for facial manipulation detection. |
ANDREAS ROSSLER et. al. | iccv | 2019-10-24 |
| 124 | The Deepfake Detection Challenge (DFDC) Preview Dataset IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a preview of the Deepfakes Detection Challenge (DFDC) dataset consisting of 5K videos featuring two facial modification algorithms. |
Brian Dolhansky; Russ Howes; Ben Pflaum; Nicole Baram; Cristian Canton Ferrer; | arxiv-cs.CV | 2019-10-19 |
| 125 | Adversarial Learning Of Deepfakes In Accounting IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we show an adversarial attack against CAATs using deep neural networks. |
Marco Schreyer; Timur Sattarov; Bernd Reimer; Damian Borth; | arxiv-cs.LG | 2019-10-09 |
| 126 | Deep Learning For Deepfakes Creation And Detection: A Survey IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present extensive discussions on challenges, research trends and directions related to deepfake technologies. |
Thanh Thi Nguyen; Cuong M. Nguyen; Dung Tien Nguyen; Duc Thanh Nguyen; Saeid Nahavandi; | arxiv-cs.CV | 2019-09-25 |
| 127 | Differential Imaging Forensics IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce some new forensics based on differential imaging, where a novel category of visual evidence created via subtle interactions of light with a scene, such as dim reflections, can be computationally extracted and amplified from an image of interest through a comparative analysis with an additional reference baseline image acquired under similar conditions. |
Aurélien Bourquard; Jeff Yan; | arxiv-cs.CR | 2019-06-12 |
| 128 | Fake News Detection Using Deep Markov Random Fields IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome this limitation, we develop a graph-theoretic method that inherits the power of deep learning while at the same time utilizing the correlations among the articles. |
Duc Minh Nguyen; Tien Huu Do; Robert Calderbank; Nikos Deligiannis,; | naacl | 2019-06-02 |
| 129 | Exposing Deep Fakes Using Inconsistent Head Poses IF:4 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new method to expose AI-generated fake face images or videos (commonly known as the Deep Fakes). |
X. Yang; Y. Li and S. Lyu; | icassp | 2019-05-11 |
| 130 | Limits Of Deepfake Detection: A Robust Estimation Viewpoint IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: Deepfake detection is formulated as a hypothesis testing problem to classify an image as genuine or GAN-generated. A robust statistics view of GANs is considered to bound the … |
Sakshi Agarwal; Lav R. Varshney; | arxiv-cs.LG | 2019-05-09 |
| 131 | Source Generator Attribution Via Inversion IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We address the problem of attributing a synthetic image to a specific generator in a white box setting, by inverting the process of generation. |
Michael Albright; Scott McCloskey; | arxiv-cs.CV | 2019-05-06 |
| 132 | Recurrent Convolutional Strategies For Face Manipulation Detection In Videos IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we attempt to detect Deepfake, Face2Face and FaceSwap tampered faces in video streams. |
EKRAAM SABIR et. al. | arxiv-cs.CV | 2019-05-02 |
| 133 | Fake News, Disinformation, And Deepfakes: Leveraging Distributed Ledger Technologies And Blockchain To Combat Digital Deception And Counterfeit Reality IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: The rise of ubiquitous deepfakes, misinformation, disinformation, propaganda and post-truth, often referred to as fake news, raises concerns over the role of Internet and social … |
Paula Fraga-Lamas; Tiago M. Fernández-Caramés; | arxiv-cs.CY | 2019-04-10 |
| 134 | Detecting GAN Generated Fake Images Using Co-occurrence Matrices IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel approach to detect GAN generated fake images using a combination of co-occurrence matrices and deep learning. |
LAKSHMANAN NATARAJ et. al. | arxiv-cs.CV | 2019-03-15 |
| 135 | FaceForensics++: Learning To Detect Manipulated Facial Images IF:5 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To standardize the evaluation of detection methods, we propose an automated benchmark for facial manipulation detection. |
ANDREAS RÖSSLER et. al. | arxiv-cs.CV | 2019-01-25 |
| 136 | FakeCatcher: Detection Of Synthetic Portrait Videos Using Biological Signals IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel approach to detect synthetic content in portrait videos, as a preventive solution for the emerging threat of deep fakes. Lastly, we release an in the wild dataset of fake portrait videos that we collected as a part of our evaluation process. |
Umur Aybars Ciftci; Ilke Demir; | arxiv-cs.CV | 2019-01-08 |
| 137 | DeepFakes: A New Threat To Face Recognition? Assessment And Detection IF:4 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To help developing such methods, in this paper, we present the first publicly available set of Deepfake videos generated from videos of VidTIMIT database. |
Pavel Korshunov; Sebastien Marcel; | arxiv-cs.CV | 2018-12-20 |
| 138 | Exposing Deep Fakes Using Inconsistent Head Poses IF:4 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new method to expose AI-generated fake face images or videos (commonly known as the Deep Fakes). |
Xin Yang; Yuezun Li; Siwei Lyu; | arxiv-cs.CV | 2018-11-01 |
| 139 | Exposing DeepFake Videos By Detecting Face Warping Artifacts IF:4 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we describe a new deep learning based method that can effectively distinguish AI-generated fake videos (referred to as {\em DeepFake} videos hereafter) from real videos. |
Yuezun Li; Siwei Lyu; | arxiv-cs.CV | 2018-11-01 |
| 140 | In Ictu Oculi: Exposing AI Generated Fake Face Videos By Detecting Eye Blinking IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we describe a new method to expose fake face videos generated with neural networks. |
Yuezun Li; Ming-Ching Chang; Siwei Lyu; | arxiv-cs.CV | 2018-06-07 |
| 141 | Mixed Deep Learning And Natural Language Processing Method For Fake-food Image Recognition And Standardization To Help Automated Dietary Assessment IF:3 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: The present study tested the combination of an established and a validated food-choice research method (the ‘fake food buffet’) with a new food-matching technology to automate the … |
Simon Mezgec; Tome Eftimov; Tamara Bucher; Barbara Koroušić Seljak; | Public health nutrition | 2018-04-06 |
| 142 | Regularizing Deep Hashing Networks Using GAN Generated Fake Images IF:2 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, in this paper, we propose a simple two-stage pipeline to learn deep hashing models, by regularizing the deep hashing networks using fake images. |
Libing Geng; Yan Pan; Jikai Chen; Hanjiang Lai; | arxiv-cs.CV | 2018-03-26 |
| 143 | In Ictu Oculi: Exposing AI Created Fake Videos By Detecting Eye Blinking IF:4 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: The new developments in deep generative networks have significantly improve the quality and efficiency in generating realistically-looking fake face videos. In this work, we … |
Yuezun Li; Ming-Ching Chang; Siwei Lyu; | 2018 IEEE International Workshop on Information Forensics … | 2018-01-01 |
| 144 | MesoNet: A Compact Facial Video Forgery Detection Network IF:4 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: This paper presents a method to automatically and efficiently detect face tampering in videos, and particularly focuses on two recent techniques used to generate hyper-realistic … |
Darius Afchar; Vincent Nozick; Junichi Yamagishi; Isao Echizen; | 2018 IEEE International Workshop on Information Forensics … | 2018-01-01 |
| 145 | Deepfake Video Detection Using Recurrent Neural Networks IF:4 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: In recent months a machine learning based free software tool has made it easy to create believable face swaps in videos that leaves few traces of manipulation, in what are known … |
David Guera; Edward J. Delp; | 2018 15th IEEE International Conference on Advanced Video … | 2018-01-01 |
| 146 | Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security IF:4 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: Harmful lies are nothing new. But the ability to distort reality has taken an exponential leap forward with “deep fake” technology. This capability makes it possible to create … |
Robert Chesney; Danielle Keats Citron; Danielle Keats Citron; Danielle Keats Citron; | California Law Review | 2018-01-01 |
| 147 | An Original Face Anti-spoofing Approach Using Partial Convolutional Neural Network IF:4 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Abstract: Recently deep Convolutional Neural Networks have been successfully applied in many computer vision tasks and achieved promising results. So some works have introduced the deep … |
LEI LI et. al. | 2016 Sixth International Conference on Image Processing … | 2016-01-01 |