The Creation and Detection of Deepfakes

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 easy it is to use this technology for unethical and malicious applications, such as the spread of misinformation, impersonation of political leaders, and the defamation of innocent individuals. Since then, these “deepfakes” have advanced significantly. In this article, we explore the creation and detection of deepfakes and provide an in-depth view as to how these architectures work. The purpose of this survey is to provide the reader with a deeper understanding of (1) how deepfakes are created and detected, (2) the current trends and advancements in this domain, (3) the shortcomings of the current defense solutions, and (4) the areas that require further research and attention.

[1]  Victor Lempitsky,et al.  Photorealistic Monocular Gaze Redirection Using Machine Learning , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Martin Kersner,et al.  MarioNETte: Few-shot Face Reenactment Preserving Identity of Unseen Targets , 2019, AAAI.

[3]  Iacopo Masi,et al.  Two-branch Recurrent Network for Isolating Deepfakes in Videos , 2020, ECCV.

[4]  Alexei A. Efros,et al.  CNN-Generated Images Are Surprisingly Easy to Spot… for Now , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Felix Juefei-Xu,et al.  FakeSpotter: A Simple Baseline for Spotting AI-Synthesized Fake Faces , 2019, arXiv.org.

[6]  W. Golder Facing the Facts Über den Schatten springen , 2017, RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren.

[7]  Tiago M. Fernández-Caramés,et al.  Leveraging Distributed Ledger Technologies and Blockchain to Combat Fake News , 2019, ArXiv.

[8]  Yuval Elovici,et al.  CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learning , 2019, USENIX Security Symposium.

[9]  Yong Yu,et al.  Face Transfer with Generative Adversarial Network , 2017, ArXiv.

[10]  Xiaoming Liu,et al.  Representation Learning by Rotating Your Faces , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Joon Son Chung,et al.  You Said That?: Synthesising Talking Faces from Audio , 2019, International Journal of Computer Vision.

[12]  Fang Wen,et al.  FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping , 2019, ArXiv.

[13]  Maja Pantic,et al.  Speech-Driven Facial Animation Using Polynomial Fusion of Features , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[14]  Esa Rahtu,et al.  ICface: Interpretable and Controllable Face Reenactment Using GANs , 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[15]  Tero Karras,et al.  Analyzing and Improving the Image Quality of StyleGAN , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Justus Thies,et al.  Deferred neural rendering , 2019, ACM Trans. Graph..

[17]  Hang Zhou,et al.  Talking Face Generation by Adversarially Disentangled Audio-Visual Representation , 2018, AAAI.

[18]  Frédo Durand,et al.  Synthesizing Images of Humans in Unseen Poses , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[19]  Bolei Zhou,et al.  FaceFeat-GAN: a Two-Stage Approach for Identity-Preserving Face Synthesis , 2018, ArXiv.

[20]  Zhenan Sun,et al.  Pose-Guided Photorealistic Face Rotation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[21]  Mei Wang,et al.  Deep Face Recognition: A Survey , 2018, Neurocomputing.

[22]  Kiran B. Raja,et al.  Fake Face Detection Methods: Can They Be Generalized? , 2018, 2018 International Conference of the Biometrics Special Interest Group (BIOSIG).

[23]  Mario Fritz,et al.  Attributing Fake Images to GANs: Learning and Analyzing GAN Fingerprints , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[24]  M. Tesconi,et al.  TweepFake: About detecting deepfake tweets , 2020, PloS one.

[25]  Chen Qian,et al.  ReenactGAN: Learning to Reenact Faces via Boundary Transfer , 2018, ECCV.

[26]  Siwei Lyu,et al.  Exposing DeepFake Videos By Detecting Face Warping Artifacts , 2018, CVPR Workshops.

[27]  Thomas Huang,et al.  FLNet: Landmark Driven Fetching and Learning Network for Faithful Talking Facial Animation Synthesis , 2019, AAAI.

[28]  Jung-Woo Ha,et al.  StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[29]  Andrew H. Sung,et al.  DeepfakeStack: A Deep Ensemble-based Learning Technique for Deepfake Detection , 2020, 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom).

[30]  Davide Cozzolino,et al.  Detection of GAN-Generated Fake Images over Social Networks , 2018, 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR).

[31]  Joo-Ho Lee,et al.  Talking heads synthesis from audio with deep neural networks , 2015, 2015 IEEE/SICE International Symposium on System Integration (SII).

[32]  Sakshi Agarwal,et al.  Limits of Deepfake Detection: A Robust Estimation Viewpoint , 2019, ArXiv.

[33]  Christian Theobalt,et al.  Neural Rendering and Reenactment of Human Actor Videos , 2018, ACM Trans. Graph..

[34]  Edward J. Delp,et al.  Deepfake Video Detection Using Recurrent Neural Networks , 2018, 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[35]  Hamid Aghajan,et al.  Speech-Driven Facial Reenactment Using Conditional Generative Adversarial Networks , 2018, ArXiv.

[36]  Hao Li,et al.  paGAN: real-time avatars using dynamic textures , 2019, ACM Trans. Graph..

[37]  Iasonas Kokkinos,et al.  Dense Pose Transfer , 2018, ECCV.

[38]  Cristian Canton-Ferrer,et al.  The Deepfake Detection Challenge (DFDC) Preview Dataset , 2019, ArXiv.

[39]  Sébastien Marcel,et al.  Speaker Inconsistency Detection in Tampered Video , 2018, 2018 26th European Signal Processing Conference (EUSIPCO).

[40]  Shiva K. Pentyala,et al.  Towards Generalizable Forgery Detection with Locality-aware AutoEncoder , 2019, ArXiv.

[41]  Headon , 2018, ACM Transactions on Graphics.

[42]  Junichi Yamagishi,et al.  MesoNet: a Compact Facial Video Forgery Detection Network , 2018, 2018 IEEE International Workshop on Information Forensics and Security (WIFS).

[43]  Junichi Yamagishi,et al.  Capsule-forensics: Using Capsule Networks to Detect Forged Images and Videos , 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[44]  Siwei Lyu,et al.  In Ictu Oculi: Exposing AI Created Fake Videos by Detecting Eye Blinking , 2018, 2018 IEEE International Workshop on Information Forensics and Security (WIFS).

[45]  Antitza Dantcheva,et al.  ImaGINator: Conditional Spatio-Temporal GAN for Video Generation , 2020, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[46]  Shigeo Morishima,et al.  RSGAN: face swapping and editing using face and hair representation in latent spaces , 2018, SIGGRAPH Posters.

[47]  Chao Yang,et al.  Realistic Dynamic Facial Textures from a Single Image Using GANs , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[48]  Premkumar Natarajan,et al.  Recurrent Convolutional Strategies for Face Manipulation Detection in Videos , 2019, CVPR Workshops.

[49]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[50]  ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection , 2018, ArXiv.

[51]  Bertram E. Shi,et al.  Photorealistic facial expression synthesis by the conditional difference adversarial autoencoder , 2017, 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII).

[52]  Yong Jae Lee,et al.  MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation , 2019, Computer Vision and Pattern Recognition.

[53]  Sean Franklin,et al.  Deepfake Detection using Spatiotemporal Convolutional Networks , 2020, ArXiv.

[54]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[55]  Anil K. Jain,et al.  On the Detection of Digital Face Manipulation , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[56]  Chih-Chung Hsu,et al.  Deep Fake Image Detection Based on Pairwise Learning , 2020, Applied Sciences.

[57]  Roberto Caldelli,et al.  Exploiting Prediction Error Inconsistencies through LSTM-based Classifiers to Detect Deepfake Videos , 2020, IH&MMSec.

[58]  Wojciech Zaremba,et al.  Improved Techniques for Training GANs , 2016, NIPS.

[59]  Miao Yu,et al.  Progressive Pose Attention Transfer for Person Image Generation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[60]  Ilke Demir,et al.  How Do the Hearts of Deep Fakes Beat? Deep Fake Source Detection via Interpreting Residuals with Biological Signals , 2020, 2020 IEEE International Joint Conference on Biometrics (IJCB).

[61]  Kun Zhou,et al.  Warp-guided GANs for single-photo facial animation , 2018, ACM Trans. Graph..

[62]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[63]  Honggang Qi,et al.  Celeb-DF: A New Dataset for DeepFake Forensics , 2019, ArXiv.

[64]  Yang Liu,et al.  FakeSpotter: A Simple yet Robust Baseline for Spotting AI-Synthesized Fake Faces , 2019, IJCAI.

[65]  Ira Kemelmacher-Shlizerman,et al.  Transfiguring portraits , 2016, ACM Trans. Graph..

[66]  Zhenan Sun,et al.  3D Aided Duet GANs for Multi-View Face Image Synthesis , 2019, IEEE Transactions on Information Forensics and Security.

[67]  Philip H. S. Torr,et al.  A Conditional Deep Generative Model of People in Natural Images , 2019, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).

[68]  Bolin Chen,et al.  Fake Faces Identification via Convolutional Neural Network , 2018, IH&MMSec.

[69]  Aniket Bera,et al.  Emotions Don't Lie: An Audio-Visual Deepfake Detection Method using Affective Cues , 2020, ACM Multimedia.

[70]  Michel F. Valstar,et al.  Triple consistency loss for pairing distributions in GAN-based face synthesis , 2018, ArXiv.

[71]  Jeremy Straub,et al.  Using subject face brightness assessment to detect ‘deep fakes’ (Conference Presentation) , 2019, Real-Time Image Processing and Deep Learning 2019.

[72]  Richa Singh,et al.  SWAPPED! Digital face presentation attack detection via weighted local magnitude pattern , 2017, 2017 IEEE International Joint Conference on Biometrics (IJCB).

[73]  Qiang Ling,et al.  Mining Audio, Text and Visual Information for Talking Face Generation , 2019, 2019 IEEE International Conference on Data Mining (ICDM).

[74]  Sébastien Marcel,et al.  DeepFakes: a New Threat to Face Recognition? Assessment and Detection , 2018, ArXiv.

[75]  Yong Jae Lee,et al.  Identity From Here, Pose From There: Self-Supervised Disentanglement and Generation of Objects Using Unlabeled Videos , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[76]  Maneesh Agrawala,et al.  Detecting Deep-Fake Videos from Phoneme-Viseme Mismatches , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[77]  Sunny Raj,et al.  Detecting Deepfake Videos using Attribution-Based Confidence Metric , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[78]  Nicu Sebe,et al.  Deformable GANs for Pose-Based Human Image Generation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[79]  Ben Y. Zhao,et al.  Fawkes: Protecting Privacy against Unauthorized Deep Learning Models , 2020, USENIX Security Symposium.

[80]  Jia Li,et al.  Zooming into Face Forensics: A Pixel-level Analysis , 2019, ArXiv.

[81]  Simon S. Woo,et al.  OC-FakeDect: Classifying Deepfakes Using One-class Variational Autoencoder , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[82]  Patrick D. McDaniel,et al.  Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples , 2016, ArXiv.

[83]  Nicu Sebe,et al.  First Order Motion Model for Image Animation , 2020, NeurIPS.

[84]  Ira Kemelmacher-Shlizerman,et al.  Synthesizing Obama , 2017, ACM Trans. Graph..

[85]  Sina Honari,et al.  Unsupervised Depth Estimation, 3D Face Rotation and Replacement , 2018, NeurIPS.

[86]  Bernt Schiele,et al.  A Hybrid Model for Identity Obfuscation by Face Replacement , 2018, ECCV.

[87]  Jingwen Zhu,et al.  Talking Face Generation by Conditional Recurrent Adversarial Network , 2018, IJCAI.

[88]  Fumin Shen,et al.  Make a Face: Towards Arbitrary High Fidelity Face Manipulation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[89]  Justus Thies,et al.  Real-time expression transfer for facial reenactment , 2015, ACM Trans. Graph..

[90]  Shiguo Lian,et al.  Video Synthesis of Human Upper Body with Realistic Face , 2019, 2019 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct).

[91]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[92]  Yaser Sheikh,et al.  Recycle-GAN: Unsupervised Video Retargeting , 2018, ECCV.

[93]  C. Aggarwal Neural Networks and Deep Learning: A Textbook , 2018 .

[94]  Iasonas Kokkinos,et al.  DensePose: Dense Human Pose Estimation in the Wild , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[95]  Patrick Nguyen,et al.  Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis , 2018, NeurIPS.

[96]  F. Koushanfar,et al.  Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples , 2020, 2021 IEEE Winter Conference on Applications of Computer Vision (WACV).

[97]  Adam Finkelstein,et al.  Text-based editing of talking-head video , 2019, ACM Trans. Graph..

[98]  Simon S. Woo,et al.  Detecting Both Machine and Human Created Fake Face Images In the Wild , 2018, MPS@CCS.

[99]  Yoshua Bengio,et al.  Char2Wav: End-to-End Speech Synthesis , 2017, ICLR.

[100]  Iacopo Masi,et al.  Recurrent-Convolution Approach to DeepFake Detection - State-Of-Art Results on FaceForensics++ , 2019 .

[101]  Shree K. Nayar,et al.  Face swapping: automatically replacing faces in photographs , 2008, SIGGRAPH 2008.

[102]  Justus Thies,et al.  Headon , 2018, ACM Trans. Graph..

[103]  Xia Hu,et al.  Towards Generalizable Deepfake Detection with Locality-aware AutoEncoder , 2019, CIKM.

[104]  Daniel Cohen-Or,et al.  Bringing portraits to life , 2017, ACM Trans. Graph..

[105]  Francesc Moreno-Noguer,et al.  GANimation: One-Shot Anatomically Consistent Facial Animation , 2019, International Journal of Computer Vision.

[106]  Tony Ezzat,et al.  Transferable videorealistic speech animation , 2005, SCA '05.

[107]  Andrew Zisserman,et al.  X2Face: A network for controlling face generation by using images, audio, and pose codes , 2018, ECCV.

[108]  Li Fei-Fei,et al.  Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.

[109]  Khaled Salah,et al.  Combating Deepfake Videos Using Blockchain and Smart Contracts , 2019, IEEE Access.

[110]  Chenliang Xu,et al.  Hierarchical Cross-Modal Talking Face Generation With Dynamic Pixel-Wise Loss , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[111]  Patrick Pérez,et al.  Automatic Face Reenactment , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[112]  Hany Farid,et al.  Evading Deepfake-Image Detectors with White- and Black-Box Attacks , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[113]  Xiaogang Wang,et al.  FaceID-GAN: Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[114]  Hai Xuan Pham,et al.  Generative Adversarial Talking Head: Bringing Portraits to Life with a Weakly Supervised Neural Network , 2018, ArXiv.

[115]  Tal Hassner,et al.  DeepFake Detection Based on Discrepancies Between Faces and Their Context , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[116]  Hao Li,et al.  Protecting World Leaders Against Deep Fakes , 2019, CVPR Workshops.

[117]  Ali Farhadi,et al.  Defending Against Neural Fake News , 2019, NeurIPS.

[118]  Thomas Vetter,et al.  A morphable model for the synthesis of 3D faces , 1999, SIGGRAPH.

[119]  Maja Pantic,et al.  End-to-End Speech-Driven Realistic Facial Animation with Temporal GANs , 2019, CVPR Workshops.

[120]  D. Ramanan,et al.  MetaPix: Few-Shot Video Retargeting , 2019, ICLR.

[121]  Stefano Berretti,et al.  Dynamic Facial Expression Generation on Hilbert Hypersphere With Conditional Wasserstein Generative Adversarial Nets , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[122]  Andreas Rössler,et al.  FaceForensics++: Learning to Detect Manipulated Facial Images , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[123]  Mei Xie,et al.  Deep transfer across domains for face antispoofing , 2019, J. Electronic Imaging.

[124]  Kyoung Mu Lee,et al.  Accurate Image Super-Resolution Using Very Deep Convolutional Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[125]  Chen Fang,et al.  Dance Dance Generation: Motion Transfer for Internet Videos , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

[126]  Shao-Liang Chang,et al.  A Trusting News Ecosystem Against Fake News from Humanity and Technology Perspectives , 2019, 2019 19th International Conference on Computational Science and Its Applications (ICCSA).

[127]  Tal Hassner,et al.  On Face Segmentation, Face Swapping, and Face Perception , 2017, 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018).

[128]  Justus Thies,et al.  Neural Voice Puppetry: Audio-driven Facial Reenactment , 2019, ECCV.

[129]  Andreas Rössler,et al.  FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces , 2018, ArXiv.

[130]  Yu Ding,et al.  FaceSwapNet: Landmark Guided Many-to-Many Face Reenactment , 2019, ArXiv.

[131]  Tomaso A. Poggio,et al.  Reanimating Faces in Images and Video , 2003, Comput. Graph. Forum.

[132]  Ying Zhang,et al.  Automated face swapping and its detection , 2017, 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP).

[133]  Victor S. Lempitsky,et al.  DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation , 2016, ECCV.

[134]  Julian Togelius,et al.  DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution* , 2017, 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS).

[135]  Justus Thies,et al.  Face2Face: real-time face capture and reenactment of RGB videos , 2019, Commun. ACM.

[136]  Sridha Sridharan,et al.  Exploiting Human Social Cognition for the Detection of Fake and Fraudulent Faces via Memory Networks , 2019, ArXiv.

[137]  Jan Kautz,et al.  Few-shot Video-to-Video Synthesis , 2019, NeurIPS.

[138]  Luisa Verdoliva,et al.  Do GANs Leave Artificial Fingerprints? , 2018, 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR).

[139]  Yan Wang,et al.  Fighting Against Deepfake: Patch&Pair Convolutional Neural Networks (PPCNN) , 2020, WWW.

[140]  Ying Zhang,et al.  A survey on image tampering and its detection in real-world photos , 2019, J. Vis. Commun. Image Represent..

[141]  Dani Lischinski,et al.  Deep Video‐Based Performance Cloning , 2018, Comput. Graph. Forum.

[142]  Robert M. Chesney,et al.  Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security , 2018 .

[143]  Alexei A. Efros,et al.  Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[144]  Hanspeter Pfister,et al.  Face transfer with multilinear models , 2005, SIGGRAPH 2005.

[145]  Gang Hua,et al.  Towards Open-Set Identity Preserving Face Synthesis , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[146]  Jan Kautz,et al.  Video-to-Video Synthesis , 2018, NeurIPS.

[147]  Sergey Levine,et al.  Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.

[148]  Xingming Sun,et al.  Fake Face Detection via Adaptive Residuals Extraction Network , 2020, ArXiv.

[149]  Holly Kathleen Hall Deepfake Videos: When Seeing Isn't Believing , 2018 .

[150]  Dipankar Dasgupta,et al.  A Comparative Evaluation of Local Feature Descriptors for DeepFakes Detection , 2019, 2019 IEEE International Symposium on Technologies for Homeland Security (HST).

[151]  Shigeo Morishima,et al.  FSNet: An Identity-Aware Generative Model for Image-based Face Swapping , 2018, ACCV.

[152]  Alberto Del Bimbo,et al.  Deepfake Video Detection through Optical Flow Based CNN , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

[153]  Junichi Yamagishi,et al.  Multi-task Learning for Detecting and Segmenting Manipulated Facial Images and Videos , 2019, 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS).

[154]  Fang Wen,et al.  Face X-Ray for More General Face Forgery Detection , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[155]  Pavel Korshunov,et al.  Tampered Speaker Inconsistency Detection with Phonetically Aware Audio-visual Features , 2019, ICML 2019.

[156]  Jing Dong,et al.  On the generalization of GAN image forensics , 2019, CCBR.

[157]  Siwei Zhang,et al.  One-shot Face Reenactment , 2019, BMVC.

[158]  Giulia Boato,et al.  Physiologically-based detection of computer generated faces in video , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[159]  J. Keuper,et al.  Unmasking DeepFakes with simple Features , 2019, ArXiv.

[160]  M. Workman Wisecrackers: A theory-grounded investigation of phishing and pretext social engineering threats to information security , 2008 .

[161]  Harshad Rai,et al.  Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks , 2018 .

[162]  Baoyuan Wu,et al.  Hiding Faces in Plain Sight: Disrupting AI Face Synthesis with Adversarial Perturbations , 2019, ArXiv.

[163]  Qian Zhang,et al.  High Fidelity Face Manipulation with Extreme Pose and Expression , 2019, ArXiv.

[164]  Yoshua Bengio,et al.  ObamaNet: Photo-realistic lip-sync from text , 2017, ArXiv.

[165]  Leonid Sigal,et al.  DwNet: Dense warp-based network for pose-guided human video generation , 2019, BMVC.

[166]  Alexei A. Efros,et al.  Everybody Dance Now , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[167]  Ilke Demir,et al.  FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals , 2019, IEEE transactions on pattern analysis and machine intelligence.

[168]  Gang Hua,et al.  CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[169]  Jitendra Malik,et al.  End-to-End Recovery of Human Shape and Pose , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[170]  Jan Kautz,et al.  MoCoGAN: Decomposing Motion and Content for Video Generation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[171]  Luc Van Gool,et al.  Disentangled Person Image Generation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[172]  Damian Borth,et al.  Adversarial Learning of Deepfakes in Accounting , 2019, ArXiv.

[173]  Tiago M. Fernández-Caramés,et al.  Fake News, Disinformation, and Deepfakes: Leveraging Distributed Ledger Technologies and Blockchain to Combat Digital Deception and Counterfeit Reality , 2019, IT Professional.

[174]  V. Lempitsky,et al.  Few-Shot Adversarial Learning of Realistic Neural Talking Head Models , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[175]  Nicu Sebe,et al.  Animating Arbitrary Objects via Deep Motion Transfer , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[176]  Xin Yang,et al.  Exposing Deep Fakes Using Inconsistent Head Poses , 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[177]  Tal Hassner,et al.  FSGAN: Subject Agnostic Face Swapping and Reenactment , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[178]  Patrick Pérez,et al.  Deep video portraits , 2018, ACM Trans. Graph..

[179]  Maja Pantic,et al.  Realistic Speech-Driven Facial Animation with GANs , 2019, International Journal of Computer Vision.

[180]  Jan Kautz,et al.  High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[181]  Hans-Peter Seidel,et al.  Exchanging Faces in Images , 2004, Comput. Graph. Forum.

[182]  Wojciech Matusik,et al.  Video face replacement , 2011, ACM Trans. Graph..

[183]  Leon A. Gatys,et al.  A Neural Algorithm of Artistic Style , 2015, ArXiv.

[184]  Eric C. Larson,et al.  Swapped face detection using deep learning and subjective assessment , 2019, EURASIP Journal on Information Security.

[185]  Wenhan Luo,et al.  Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[186]  Gang Liu,et al.  Improving Few-Shot User-Specific Gaze Adaptation via Gaze Redirection Synthesis , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[187]  Lucas Theis,et al.  Fast Face-Swap Using Convolutional Neural Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).