11.14.4.5.1 Deepfakes, Face Synthesis, Fake News

Chapter Contents (Back)
Deepfakes. Deep Fakes. Fakes.
See also Liveness Detection, Spoofing, Presentation Attack, Faces, Other Biometrics.
See also Forgery Detection for Images.

Cybenko, A.K., Cybenko, G.,
AI and Fake News,
IEEE_Int_Sys(33), No. 5, Sep. 2018, pp. 1-5.
IEEE DOI 1901
Cultural differences, Intelligent systems, Social network services, Psychology, Economics, Software BibRef

de Rezende, E.R.S.[Edmar R.S.], Ruppert, G.C.S.[Guilherme C.S.], Theˇphilo, A.[Ant˘nio], Tokuda, E.K.[Eric K.], Carvalho, T.[Tiago],
Exposing computer generated images by using deep convolutional neural networks,
SP:IC(66), 2018, pp. 113-126.
Elsevier DOI 1806
Digital forensics, CG detection, Deep learning, Transfer learning, Fake news BibRef

Reis, J.C.S., Correia, A., Murai, F., Veloso, A., Benevenuto, F., Cambria, E.,
Supervised Learning for Fake News Detection,
IEEE_Int_Sys(34), No. 2, March 2019, pp. 76-81.
IEEE DOI 1906
Feature extraction, Facebook, Data mining, IP networks, Radio frequency, Affective computing BibRef

Greengard, S.[Samuel],
Will Deepfakes Do Deep Damage?,
CACM(63), No. 1, January 2020, pp. 17-19.
DOI Link 1912
News item. BibRef

Strickland, E.,
Facebook takes on deepfakes,
Spectrum(57), No. 01, January 2020, pp. 40-57.
IEEE DOI 2001
They are everywhere, they are high quality. BibRef

Babaguchi, N.[Noboru], Echizen, I.[Isao], Yamagishi, J.[Junichi], Nitta, N.[Naoko], Nakashima, Y.[Yuta], Nakamura, K.[Kazuaki], Kono, K.[Kazuhiro], Fang, F.M.[Fu-Ming], Myojin, S.[Seiko], Kuang, Z.Z.[Zhen-Zhong], Nguyen, H.H.[Huy H.], Tieu, N.D.T.[Ngoc-Dung T.],
Preventing Fake Information Generation Against Media Clone Attacks,
IEICE(E104-D), No. 1, January 2021, pp. 2-11.
WWW Link. 2101
BibRef

de Oliveira, N.R., Medeiros, D.S.V., Mattos, D.M.F.,
A Sensitive Stylistic Approach to Identify Fake News on Social Networking,
SPLetters(27), 2020, pp. 1250-1254.
IEEE DOI 2007
Fake news detection, one-class SVM BibRef

Johnson, D.G.[Deborah G.], Diakopoulos, N.[Nicholas],
What To Do About Deepfakes,
CACM(64), No. 1, January 2021, pp. 33-35.
DOI Link 2103
Seeking to reap the positive uses of synthetic media while minimizing or preventing negative societal impact. BibRef

Kamboj, M., Hessler, C., Asnani, P., Riani, K., Abouelenien, M.,
Multimodal Political Deception Detection,
MultMedMag(28), No. 1, January 2021, pp. 94-102.
IEEE DOI 2104
Feature extraction, Videos, Linguistics, Visualization, Psychology, Task analysis BibRef

Mirsky, Y.[Yisroel], Lee, W.[Wenke],
The Creation and Detection of Deepfakes: A Survey,
Surveys(54), No. 1, January 2021, pp. xx-yy.
DOI Link 2104
Survey, Deepfakes. impersonation, generative AI, social engineering, face swap, replacement, deep fake, Deepfake, reenactment BibRef

Caldelli, R.[Roberto], Galteri, L.[Leonardo], Amerini, I.[Irene], del Bimbo, A.[Alberto],
Optical Flow based CNN for detection of unlearnt deepfake manipulations,
PRL(146), 2021, pp. 31-37.
Elsevier DOI 2105
BibRef
Earlier: A3, A2, A1, A4:
Deepfake Video Detection through Optical Flow Based CNN,
HBU19(1205-1207)
IEEE DOI 2004
Deepfake manipulations, Optical Flow, Video forensics, CNN. convolutional neural nets, image classification, image sequences, learning (artificial intelligence), video signal processing. BibRef

Xu, Z.P.[Zhao-Peng], Liu, J.R.[Jia-Rui], Lu, W.[Wei], Xu, B.[Bozhi], Zhao, X.F.[Xian-Feng], Li, B.[Bin], Huang, J.W.[Ji-Wu],
Detecting facial manipulated videos based on set convolutional neural networks,
JVCIR(77), 2021, pp. 103119.
Elsevier DOI 2106
Digital video forensics, Deepfake, Set convolutional neural network, Set reduce BibRef

Shang, Z.H.[Zhi-Hua], Xie, H.T.[Hong-Tao], Zha, Z.J.[Zheng-Jun], Yu, L.Y.[Ling-Yun], Li, Y.[Yan], Zhang, Y.D.[Yong-Dong],
PRRNet: Pixel-Region relation network for face forgery detection,
PR(116), 2021, pp. 107950.
Elsevier DOI 2106
Face forgery detection, Forgery localization, Inconsistency detection, Relation learning BibRef

Alharbi, A.[Ahmed], Dong, H.[Hai], Yi, X.[Xun], Tari, Z.[Zahir], Khalil, I.[Ibrahim],
Social Media Identity Deception Detection: A Survey,
Surveys(54), No. 3, April 2021, pp. xx-yy.
DOI Link 2106
Identity deception, social botnet, Sybil, sockpuppet, identity theft, identity cloning, detection techniques, fake profile BibRef

Ferrara, M.[Matteo], Franco, A.[Annalisa], Maltoni, D.[Davide],
Face morphing detection in the presence of printing/scanning and heterogeneous image sources,
IET-Bio(10), No. 3, 2021, pp. 290-303.
DOI Link 2106
BibRef

Groh, M.[Matthew], Epstein, Z.[Ziv], Obradovich, N.[Nick], Cebrian, M.[Manuel], Rahwan, I.[Iyad],
Human Detection of Machine-Manipulated Media,
CACM(64), No. 10, October 2021, pp. 40-47.
DOI Link 2109
BibRef

Kim, G.[Gihwan], Ko, Y.J.[Young-Joong],
Effective fake news detection using graph and summarization techniques,
PRL(151), 2021, pp. 135-139.
Elsevier DOI 2110
Fake news detection, Graph neural networks, Summarization, Deep neural networks BibRef

Yu, P.[Peipeng], Xia, Z.H.[Zhi-Hua], Fei, J.W.[Jian-Wei], Lu, Y.J.[Yu-Jiang],
A Survey on Deepfake Video Detection,
IET-Bio(10), No. 6, 2021, pp. 607-624.
DOI Link 2110
Survey, Deepfakes. BibRef

Sabitha, R., Aruna, A., Karthik, S., Shanthini, J.,
Enhanced model for fake image detection (EMFID) using convolutional neural networks with histogram and wavelet based feature extractions,
PRL(152), 2021, pp. 195-201.
Elsevier DOI 2112
Fake image detection, Image features, Histogram based feature extraction, Convolutional neural networks (CNN) BibRef

Kong, C.Q.[Chen-Qi], Chen, B.[Baoliang], Yang, W.H.[Wen-Han], Li, H.[Haoliang], Chen, P.[Peilin], Wang, S.[Shiqi],
Appearance Matters, So Does Audio: Revealing the Hidden Face via Cross-Modality Transfer,
CirSysVideo(32), No. 1, January 2022, pp. 423-436.
IEEE DOI 2201
Faces, Videos, Information integrity, Face recognition, Training, Generative adversarial networks, Testing, Deepfake, cross modality, fake face BibRef

Choi, H.[Hyewon], Ko, Y.J.[Young-Joong],
Effective fake news video detection using domain knowledge and multimodal data fusion on youtube,
PRL(154), 2022, pp. 44-52.
Elsevier DOI 2202
Fake news video detection, Domain knowledge, Multimodal data, Deep neural networks BibRef

Hu, J.[Juan], Liao, X.[Xin], Wang, W.[Wei], Qin, Z.[Zheng],
Detecting Compressed Deepfake Videos in Social Networks Using Frame-Temporality Two-Stream Convolutional Network,
CirSysVideo(32), No. 3, March 2022, pp. 1089-1102.
IEEE DOI 2203
Videos, Information integrity, Feature extraction, Streaming media, Faces, Forensics, Social networking (online), Video forensics, temporality-level stream BibRef

Nguyen, V.H.[Van-Hoang], Sugiyama, K.[Kazunari], Nakov, P.[Preslav], Kan, M.Y.[Min-Yen],
FANG: Leveraging Social Context for Fake News Detection Using Graph Representation,
CACM(65), No. 4, April 2022, pp. 124-132.
DOI Link 2204
BibRef

Zhang, X.K.[Xiao-Kang], Zhu, Y.L.[Yuan-Lue], Chen, W.T.[Wen-Ting], Liu, W.S.[Wen-Shuang], Shen, L.L.[Lin-Lin],
Gated SwitchGAN for Multi-Domain Facial Image Translation,
MultMed(24), No. 2022, pp. 1990-2003.
IEEE DOI 2204
Switches, Generators, Logic gates, Faces, Control systems, Feature extraction, Task analysis, GANs, Image translation, Attribute intensity control BibRef

Li, W.C.[Wei-Chuang], He, P.S.[Pei-Song], Li, H.L.[Hao-Liang], Wang, H.X.[Hong-Xia], Zhang, R.M.[Rui-Mei],
Detection of GAN-Generated Images by Estimating Artifact Similarity,
SPLetters(29), No. 2022, pp. 862-866.
IEEE DOI 2204
Prototypes, Generative adversarial networks, Training, Feature extraction, Testing, Optimization, Task analysis, artifact similarity estimation BibRef

Zhang, L.H.[Long-Hao], Yang, H.H.[Hui-Hua], Qiu, T.[Tian], Li, L.Q.[Ling-Qiao],
AP-GAN: Improving Attribute Preservation in Video Face Swapping,
CirSysVideo(32), No. 4, April 2022, pp. 2226-2237.
IEEE DOI 2204
Faces, Face recognition, Facial features, Generative adversarial networks, Lighting, Generators, Skin, perceptual loss BibRef

Wang, Z.[Zhi], Guo, Y.[Yiwen], Zuo, W.M.[Wang-Meng],
Deepfake Forensics via an Adversarial Game,
IP(31), 2022, pp. 3541-3552.
IEEE DOI 2205
Training, Forgery, Faces, Task analysis, Predictive models, Face recognition, Robustness, Deepfake forensics, generalization ability BibRef

Amorese, T.[Terry], Cuciniello, M.[Marialucia], Vinciarelli, A.[Alessandro], Cordasco, G.[Gennaro], Esposito, A.[Anna],
Synthetic vs Human Emotional Faces: What Changes in Humans' Decoding Accuracy,
HMS(52), No. 3, June 2022, pp. 390-399.
IEEE DOI 2205
Faces, Emotion recognition, Decoding, Face recognition, Aging, Task analysis, Portable computers, Assistive technology, human-computer interaction BibRef

Pu, W.[Wenbo], Hu, J.[Jing], Wang, X.[Xin], Li, Y.Z.[Yue-Zun], Hu, S.[Shu], Zhu, B.[Bin], Song, R.[Rui], Song, Q.[Qi], Wu, X.[Xi], Lyu, S.W.[Si-Wei],
Learning a deep dual-level network for robust DeepFake detection,
PR(130), 2022, pp. 108832.
Elsevier DOI 2206
DeepFake detection, Multitask learning, Imbalanced learning, AUC optimization BibRef

Wei, P.F.[Peng-Fei], Wu, F.[Fei], Sun, Y.[Ying], Zhou, H.[Hong], Jing, X.Y.[Xiao-Yuan],
Modality and Event Adversarial Networks for Multi-Modal Fake News Detection,
SPLetters(29), 2022, pp. 1382-1386.
IEEE DOI 2206
Feature extraction, Fake news, Blogs, Image reconstruction, Generators, Social networking (online), Deep learning, multi-modal generator BibRef

Mishima, K.[Ken], Yamana, H.[Hayato],
A Survey on Explainable Fake News Detection,
IEICE(E105-D), No. 7, July 2022, pp. 1249-1257.
WWW Link. 2207
Survey, Fake News. BibRef

Juefei-Xu, F.[Felix], Wang, R.[Run], Huang, Y.H.[Yi-Hao], Guo, Q.[Qing], Ma, L.[Lei], Liu, Y.[Yang],
Countering Malicious DeepFakes: Survey, Battleground, and Horizon,
IJCV(130), No. 7, July 2022, pp. 1678-1734.
Springer DOI 2207
BibRef

Ding, F.[Feng], Zhu, G.P.[Guo-Pu], Li, Y.C.[Ying-Can], Zhang, X.P.[Xin-Peng], Atrey, P.K.[Pradeep K.], Lyu, S.W.[Si-Wei],
Anti-Forensics for Face Swapping Videos via Adversarial Training,
MultMed(24), 2022, pp. 3429-3441.
IEEE DOI 2207
Videos, Information integrity, Faces, Forensics, Detectors, Visualization, Tools, Digital forensics, anti-forensics, DeepFake, generative adversarial network BibRef

Li, P.G.[Pei-Guang], Sun, X.[Xian], Yu, H.F.[Hong-Feng], Tian, Y.[Yu], Yao, F.L.[Fang-Long], Xu, G.L.[Guang-Luan],
Entity-Oriented Multi-Modal Alignment and Fusion Network for Fake News Detection,
MultMed(24), 2022, pp. 3455-3468.
IEEE DOI 2207
Feature extraction, Visualization, Task analysis, Heuristic algorithms, Social networking (online), Semantics, Sun, multi-modal BibRef

Yang, X.[Xiao], Liu, S.[Shilong], Dong, Y.P.[Yin-Peng], Su, H.[Hang], Zhang, L.[Lei], Zhu, J.[Jun],
Towards generalizable detection of face forgery via self-guided model-agnostic learning,
PRL(160), 2022, pp. 98-104.
Elsevier DOI 2208
DeepFake, Face forgery detection, Face generation BibRef

Edwards, J.[John],
Applying Signal Processing to Opposite Sides of Imaging: Separate European research projects are focusing on aspects of completely real and entirely fake images [Special Reports],
SPMag(39), No. 5, September 2022, pp. 18-20.
IEEE DOI 2209
Neuroimaging, Brain models, Image processing, Distortion, Photography, Signal processing algorithms BibRef


Ramkissoon, A.N.[Amit Neil], Goodridge, W.[Wayne],
Detecting Fake News in MANET Messaging Using an Ensemble Based Computational Social System,
AI4DH22(278-289).
Springer DOI 2208
BibRef

Sciucca, L.D.[Laura Della], Mameli, M.[Marco], Balloni, E.[Emanuele], Rossi, L.[Luca], Frontoni, E.[Emanuele], Zingaretti, P.[Primo], Paolanti, M.[Marina],
FakeNED: A Deep Learning Based-System for Fake News Detection from Social Media,
ISHAPE22(303-313).
Springer DOI 2208
BibRef

Sannino, C.[Ciro], Gravina, M.[Michela], Marrone, S.[Stefano], Fiameni, G.[Giuseppe], Sansone, C.[Carlo],
LessonAble: Leveraging Deep Fakes in MOOC Content Creation,
CIAP22(I:27-37).
Springer DOI 2205
BibRef

Concas, S.[Sara], Gao, J.[Jie], Cuccu, C.[Carlo], Orr¨, G.[Giulia], Feng, X.Y.[Xiao-Yi], Marcialis, G.L.[Gian Luca], Puglisi, G.[Giovanni], Roli, F.[Fabio],
Experimental Results on Multi-modal Deepfake Detection,
CIAP22(II:164-175).
Springer DOI 2205
BibRef

Boccignone, G.[Giuseppe], Bursic, S.[Sathya], Cuculo, V.[Vittorio], D'Amelio, A.[Alessandro], Grossi, G.[Giuliano], Lanzarotti, R.[Raffaella], Patania, S.[Sabrina],
DeepFakes Have No Heart: A Simple rPPG-Based Method to Reveal Fake Videos,
CIAP22(II:186-195).
Springer DOI 2205
BibRef

Coccomini, D.A.[Davide Alessandro], Messina, N.[Nicola], Gennaro, C.[Claudio], Falchi, F.[Fabrizio],
Combining EfficientNet and Vision Transformers for Video Deepfake Detection,
CIAP22(III:219-229).
Springer DOI 2205
BibRef

Guarnera, L.[Luca], Giudice, O.[Oliver], Battiato, S.[Sebastiano],
Deepfake Style Transfer Mixture: A First Forensic Ballistics Study on Synthetic Images,
CIAP22(II:151-163).
Springer DOI 2205
BibRef

Li, B.[Boqun], Qian, Z.[Zhong], Li, P.F.[Pei-Feng], Zhu, Q.M.[Qiao-Ming],
Multi-Modal Fusion Network for Rumor Detection with Texts and Images,
MMMod22(I:15-27).
Springer DOI 2203
Best paper section BibRef

Vo, N.H.[Ngan Hoang], Phan, K.D.[Khoa D.], Tran, A.D.[Anh-Duy], Dang-Nguyen, D.T.[Duc-Tien],
Adversarial Attacks on Deepfake Detectors: A Practical Analysis,
MMMod22(II:318-330).
Springer DOI 2203
BibRef

Le, T.N.[Trung-Nghia], Nguyen, H.H.[Huy H.], Yamagishi, J.[Junichi], Echizen, I.[Isao],
OpenForensics: Large-Scale Challenging Dataset For Multi-Face Forgery Detection And Segmentation In-The-Wild,
ICCV21(10097-10107)
IEEE DOI 2203
Annotations, Social networking (online), Face recognition, Media, Forgery, Face detection, Task analysis, Datasets and evaluation, Image and video manipulation detection and integrity methods. BibRef

Kwon, P.[Patrick], You, J.[Jaeseong], Nam, G.[Gyuhyeon], Park, S.[Sungwoo], Chae, G.[Gyeongsu],
KoDF: A Large-scale Korean DeepFake Detection Dataset,
ICCV21(10724-10733)
IEEE DOI 2203
Databases, Metadata, Faces, Videos, Information integrity, Datasets and evaluation, Faces, Image and video synthesis BibRef

Yu, N.[Ning], Skripniuk, V.[Vladislav], Abdelnabi, S.[Sahar], Fritz, M.[Mario],
Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data,
ICCV21(14428-14437)
IEEE DOI 2203
Visualization, Technological innovation, Computational modeling, Weapons, Training data, Fingerprint recognition, Data models, Image and video manipulation detection and integrity methods. BibRef

Zhou, Y.[Yipin], Lim, S.N.[Ser-Nam],
Joint Audio-Visual Deepfake Detection,
ICCV21(14780-14789)
IEEE DOI 2203
Deep learning, Visualization, Computational modeling, Synchronization, Speech synthesis, Task analysis, Vision + other modalities BibRef

Zhao, T.[Tianchen], Xu, X.[Xiang], Xu, M.Z.[Ming-Ze], Ding, H.[Hui], Xiong, Y.J.[Yuan-Jun], Xia, W.[Wei],
Learning Self-Consistency for Deepfake Detection,
ICCV21(15003-15013)
IEEE DOI 2203
Training, Representation learning, Image synthesis, Training data, Feature extraction, Generators, Forgery, Fairness, accountability, Faces BibRef

Cozzolino, D.[Davide], R÷ssler, A.[Andreas], Thies, J.[Justus], Nie▀ner, M.[Matthias], Verdoliva, L.[Luisa],
ID-Reveal: Identity-aware DeepFake Video Detection,
ICCV21(15088-15097)
IEEE DOI 2203
Training, Image coding, Social networking (online), Training data, Robustness, Forgery, Faces, and ethics in vision BibRef

Kim, D.K.[Dong-Keon], Kim, K.[Kwangsu],
Generalized Facial Manipulation Detection with Edge Region Feature Extraction,
WACV22(2784-2794)
IEEE DOI 2202
Image color analysis, Image edge detection, Forensics, Fingerprint recognition, Feature extraction, Robustness, Forgery, Privacy and Ethics in Vision Biometrics BibRef

Xu, Y.[Ying], Raja, K.[Kiran], Pedersen, M.[Marius],
Supervised Contrastive Learning for Generalizable and Explainable DeepFakes Detection,
Explain-Bio22(379-389)
IEEE DOI 2202
Training, Analytical models, Visualization, Fuses, Detectors, Computer architecture, Media BibRef

Jeong, Y.[Yonghyun], Kim, D.[Doyeon], Min, S.J.[Seung-Jai], Joe, S.[Seongho], Gwon, Y.[Youngjune], Choi, J.W.[Jong-Won],
BiHPF: Bilateral High-Pass Filters for Robust Deepfake Detection,
WACV22(2878-2887)
IEEE DOI 2202
Training, Frequency synthesizers, Image resolution, Image coding, Image color analysis, Computational modeling, Data models, Security/Surveillance BibRef

Wang, Y.H.[Yong-Hui], Zarghami, V.[Vahid], Cui, S.[Suxia],
Fake Face Detection using Local Binary Pattern and Ensemble Modeling,
ICIP21(3917-3921)
IEEE DOI 2201
Deep learning, Image texture, Image color analysis, Image representation, Feature extraction, Ensemble model BibRef

Wang, X.[Xiying], Ni, R.R.[Rong-Rong], Li, W.J.[Wen-Jie], Zhao, Y.[Yao],
Adversarial Attack on Fake-Faces Detectors Under White and Black Box Scenarios,
ICIP21(3627-3631)
IEEE DOI 2201
Forensics, Detectors, Generative adversarial networks, Feature extraction, Generators, Security, Adversarial attack, Forensic models BibRef

Zhou, Y.B.[Ying-Bin], Luo, A.[Anwei], Kang, X.G.[Xian-Gui], Lyu, S.W.[Si-Wei],
Face Forgery Detection Based on Segmentation Network,
ICIP21(3597-3601)
IEEE DOI 2201
Training, Knowledge engineering, Image processing, Forensics, Pipelines, Neural networks, Forgery, face swapped images/videos, frequency clues BibRef

Tauscher, J.P.[Jan-Philipp], Castillo, S.[Susana], Bosse, S.[Sebastian], Magnor, M.[Marcus],
EEG-Based Analysis of the Impact of Familiarity in the Perception of Deepfake Videos,
ICIP21(160-164)
IEEE DOI 2201
Visualization, Time-frequency analysis, Face recognition, Sociology, Observers, Media, Electroencephalography, EEG, perception, face swap BibRef

Lu, C.[Changlei], Liu, B.[Bin], Zhou, W.[Wenbo], Chu, Q.[Qi], Yu, N.H.[Neng-Hai],
Deepfake Video Detection Using 3D-Attentional Inception Convolutional Neural Network,
ICIP21(3572-3576)
IEEE DOI 2201
Solid modeling, Image processing, Data models, Security, Convolutional neural networks, Deepfake Detection, 3D Attention BibRef

Hu, J.S.[Jia-Shang], Wang, S.L.[Shi-Lin], Li, X.Y.[Xiao-Yong],
Improving the Generalization Ability of Deepfake Detection via Disentangled Representation Learning,
ICIP21(3577-3581)
IEEE DOI 2201
Deep learning, Neural networks, Feature extraction, Forgery, Data mining, Faces, Disentangle representation learning, deep neural network BibRef

Song, L.C.[Lu-Chuan], Yin, G.J.[Guo-Jun], Liu, B.[Bin], Zhang, Y.[Yuhui], Yu, N.H.[Neng-Hai],
Fsft-Net: Face Transfer Video Generation With Few-Shot Views,
ICIP21(3582-3586)
IEEE DOI 2201
Training, Deep learning, Image processing, Distortion, Generators, Task analysis, Faces, Face Transfer, Adversarial Training, Deepfake Generation BibRef

Li, G.[Gen], Cao, Y.[Yun], Zhao, X.F.[Xian-Feng],
Exploiting Facial Symmetry to Expose Deepfakes,
ICIP21(3587-3591)
IEEE DOI 2201
Feature extraction, Data models, Faces, Videos, Information integrity, Residual neural networks, angular hyperspace BibRef

Ren, Y.Z.[Yan-Zhen], Liu, W.[Wuyang], Liu, D.[Dengkai], Wang, L.[Lina],
Recalibrated Bandpass Filtering on Temporal Waveform for Audio Spoof Detection,
ICIP21(3907-3911)
IEEE DOI 2201
Filtering, Convolution, Tools, Feature extraction, Cognition, Speech synthesis, Audio spoof detection, Deepfake, ASVspoof BibRef

Liao, Q.Y.[Quan-Yu], Li, Y.Z.[Yue-Zun], Wang, X.[Xin], Kong, B.[Bin], Zhu, B.[Bin], Lyu, S.W.[Si-Wei], Yin, Y.[Youbing], Song, Q.[Qi], Wu, X.[Xi],
Imperceptible Adversarial Examples for Fake Image Detection,
ICIP21(3912-3916)
IEEE DOI 2201
Perturbation methods, Image processing, Detectors, Videos, Information integrity, Deepfake, Adversarial Example BibRef

Das, S.[Sowmen], Seferbekov, S.[Selim], Datta, A.[Arup], Islam, M.S.[Md. Saiful], Amin, M.R.[Md. Ruhul],
Towards Solving the DeepFake Problem : An Analysis on Improving DeepFake Detection using Dynamic Face Augmentation,
RPRMI21(3769-3778)
IEEE DOI 2112
Training, Pipelines, Training data, Computer architecture, Data models, Robustness, Faces BibRef

Huang, J.J.[Jia-Jun], Wang, X.[Xueyu], Du, B.[Bo], Du, P.[Pei], Xu, C.[Chang],
DeepFake MNIST+: A DeepFake Facial Animation Dataset,
AIM21(1973-1982)
IEEE DOI 2112
Image recognition, Face recognition, Detectors, Generators, Facial animation, Security BibRef

Haliassos, A.[Alexandros], Vougioukas, K.[Konstantinos], Petridis, S.[Stavros], Pantic, M.[Maja],
Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection,
CVPR21(5037-5047)
IEEE DOI 2111
Visualization, Face recognition, Perturbation methods, Semantics, Meetings, Mouth, Speech recognition BibRef

Zhao, H.Q.[Han-Qing], Wei, T.Y.[Tian-Yi], Zhou, W.[Wenbo], Zhang, W.M.[Wei-Ming], Chen, D.D.[Dong-Dong], Yu, N.H.[Neng-Hai],
Multi-attentional Deepfake Detection,
CVPR21(2185-2194)
IEEE DOI 2111
Measurement, Semantics, Feature extraction, Forgery, Pattern recognition, Feeds, Task analysis BibRef

Sun, Z.[Zekun], Han, Y.[Yujie], Hua, Z.[Zeyu], Ruan, N.[Na], Jia, W.J.[Wei-Jia],
Improving the Efficiency and Robustness of Deepfakes Detection through Precise Geometric Features,
CVPR21(3608-3617)
IEEE DOI 2111
Recurrent neural networks, Feature extraction, Robustness, Pattern recognition, Organ transplantation, Complexity theory BibRef

Chen, Z.[Zhikai], Xie, L.X.[Ling-Xi], Pang, S.[Shanmin], He, Y.[Yong], Zhang, B.[Bo],
MagDR: Mask-guided Detection and Reconstruction for Defending Deepfakes,
CVPR21(9010-9019)
IEEE DOI 2111
Visualization, Perturbation methods, Pipelines, Semantics, Prediction algorithms, Pattern recognition, Task analysis BibRef

Li, D.Z.[Dong-Ze], Wang, W.[Wei], Fan, H.X.[Hong-Xing], Dong, J.[Jing],
Exploring Adversarial Fake Images on Face Manifold,
CVPR21(5785-5794)
IEEE DOI 2111
Manifolds, Visualization, Privacy, Image forensics, Ethics, Face recognition BibRef

Black, A.[Alexander], Bui, T.[Tu], Jin, H.L.[Hai-Lin], Swaminathan, V.[Vishy], Collomosse, J.[John],
Deep Image Comparator: Learning to Visualize Editorial Change,
WMF21(972-980)
IEEE DOI 2109
Heating systems, Training, Location awareness, Degradation, Visualization, Databases, Shape BibRef

Hosler, B.[Brian], Salvi, D.[Davide], Murray, A.[Anthony], Antonacci, F.[Fabio], Bestagini, P.[Paolo], Tubaro, S.[Stefano], Stamm, M.C.[Matthew C.],
Do Deepfakes Feel Emotions? A Semantic Approach to Detecting Deepfakes Via Emotional Inconsistencies,
WMF21(1013-1022)
IEEE DOI 2109
Emotion recognition, Law, Semantics, Speech recognition, Media, Forgery BibRef

Kim, M.[Minha], Tariq, S.[Shahroz], Woo, S.S.[Simon S.],
FReTAL: Generalizing Deepfake Detection using Knowledge Distillation and Representation Learning,
WMF21(1001-1012)
IEEE DOI 2109
Adaptation models, Computational modeling, Transfer learning, Data models, Pattern recognition BibRef

Neekhara, P.[Paarth], Dolhansky, B.[Brian], Bitton, J.[Joanna], Ferrer, C.C.[Cristian Canton],
Adversarial Threats to DeepFake Detection: A Practical Perspective,
WMF21(923-932)
IEEE DOI 2109
Social networking (online), Perturbation methods, Neural networks, Multimedia Web sites, Detectors, Security BibRef

Tinsley, P.[Patrick], Czajka, A.[Adam], Flynn, P.[Patrick],
This Face Does Not Exist... But It Might Be Yours! Identity Leakage in Generative Models,
WACV21(1319-1327)
IEEE DOI 2106
Training, Image resolution, Databases, Training data, Generative adversarial networks, Data models, Faces BibRef

Trinh, L.[Loc], Tsang, M.[Michael], Rambhatla, S.[Sirisha], Liu, Y.[Yan],
Interpretable and Trustworthy Deepfake Detection via Dynamic Prototypes,
WACV21(1972-1982)
IEEE DOI 2106
Visualization, Prototypes, Detectors, Forgery, Faces BibRef

Hussain, S.[Shehzeen], Neekhara, P.[Paarth], Jere, M.[Malhar], Koushanfar, F.[Farinaz], McAuley, J.[Julian],
Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples,
WACV21(3347-3356)
IEEE DOI 2106
Industries, Perturbation methods, Pipelines, Neural networks, Detectors, Media, Video compression BibRef

Yang, S.H.[Shu-Hui], Xue, H.[Han], Ling, J.[Jun], Song, L.[Li], Xie, R.[Rong],
Deep Face Swapping via Cross-identity Adversarial Training,
MMMod21(II:74-86).
Springer DOI 2106
BibRef

Zhang, Z.[Zhewei], Mal, C.[Can], Ding, B.[Bowen], Gao, M.[Meilin],
Detecting Manipulated Facial Videos: A Time Series Solution,
ICPR21(2817-2823)
IEEE DOI 2105
Deep learning, Correlation, Face recognition, Time series analysis, Memory architecture, Physiology, Complexity theory, pattern recognization BibRef

Bonettini, N.[Nicol˛], Cannas, E.D.[Edoardo Daniele], Mandelli, S.[Sara], Bondi, L.[Luca], Bestagini, P.[Paolo], Tubaro, S.[Stefano],
Video Face Manipulation Detection Through Ensemble of CNNs,
ICPR21(5012-5019)
IEEE DOI 2105
Training, Computational modeling, Veins, Video sequences, Tools, Feature extraction, Data models, deepfake, video forensics, attention BibRef

Li, M.[Meng], Liu, B.B.[Bei-Bei], Hu, Y.J.[Yong-Jian], Wang, Y.[Yufei],
Exposing Deepfake Videos by Tracking Eye Movements,
ICPR21(5184-5189)
IEEE DOI 2105
Support vector machines, Tracking, Neural networks, Media, Tools, Feature extraction, Physiology BibRef

Liu, Y.C.[Yu-Cheng], Chang, C.M.[Chia-Ming], Chen, I.H.[I-Hsuan], Ku, Y.R.[Yu-Ru], Chen, J.C.[Jun-Cheng],
An Experimental Evaluation of Recent Face Recognition Losses for Deepfake Detection,
ICPR21(9827-9834)
IEEE DOI 2105
Face recognition, Network architecture, Data models, Task analysis, Faces, Videos, Information integrity BibRef

Masi, I.[Iacopo], Killekar, A.[Aditya], Mascarenhas, R.M.[Royston Marian], Gurudatt, S.P.[Shenoy Pratik], Abd Almageed, W.[Wael],
Two-branch Recurrent Network for Isolating Deepfakes in Videos,
ECCV20(VII:667-684).
Springer DOI 2011
BibRef

Gu, Y.W.[Ye-Wei], Zhao, X.F.[Xian-Feng], Gong, C.[Chen], Yi, X.W.[Xiao-Wei],
Deepfake Video Detection Using Audio-visual Consistency,
IWDW20(168-180).
Springer DOI 2103
BibRef

SchŘtz, M.[Mina], Schindler, A.[Alexander], Siegel, M.[Melanie], Nazemi, K.[Kawa],
Automatic Fake News Detection with Pre-trained Transformer Models,
RISS20(627-641).
Springer DOI 2103
BibRef

Tolosana, R.[Ruben], Romero-Tapiador, S.[Sergio], Fierrez, J.[Julian], Vera-Rodriguez, R.[Ruben],
Deepfakes Evolution: Analysis of Facial Regions and Fake Detection Performance,
IWBDAF20(442-456).
Springer DOI 2103
BibRef

Mishra, R.,
Fake News Detection using Higher-order User to User Mutual-attention Progression in Propagation Paths,
WMF20(2775-2783)
IEEE DOI 2008
Feature extraction, Twitter, Linear programming, Linguistics, Time series analysis BibRef

Chai, L.[Lucy], Bau, D.[David], Lim, S.N.[Ser-Nam], Isola, P.[Phillip],
What Makes Fake Images Detectable? Understanding Properties that Generalize,
ECCV20(XXVI:103-120).
Springer DOI 2011
BibRef

Zhou, J., Pun, C.M., Tong, Y.,
News Image Steganography: A Novel Architecture Facilitates the Fake News Identification,
VCIP20(235-238)
IEEE DOI 2102
Decoding, Training, Perturbation methods, Forgery, Printers, Media, image steganography, information hiding, fake news identification BibRef

Choi, D.H., Lee, H.J., Lee, S., Kim, J.U., Ro, Y.M.,
Fake Video Detection With Certainty-Based Attention Network,
ICIP20(823-827)
IEEE DOI 2011
DeepFake video detection, predictive uncertainty, certainty-key frame, certainty-based attention BibRef

Fernandes, S., Raj, S., Ewetz, R., Pannu, J.S., Jha, S.K.[S. Kumar], Ortiz, E., Vintila, I., Salter, M.,
Detecting Deepfake Videos using Attribution-Based Confidence Metric,
CVPM20(1250-1259)
IEEE DOI 2008
Videos, Face, Measurement, Computational modeling, Training data, Machine learning, Databases BibRef

Tursman, E., George, M., Kamara, S., Tompkin, J.,
Towards Untrusted Social Video Verification to Combat Deepfakes via Face Geometry Consistency,
WMF20(2784-2793)
IEEE DOI 2008
Face, Streaming media, Principal component analysis, Cameras, Mouth, Covariance matrices, Geometry BibRef

Khalid, H., Woo, S.S.,
OC-FakeDect: Classifying Deepfakes Using One-class Variational Autoencoder,
WMF20(2794-2803)
IEEE DOI 2008
Face, Training, Image reconstruction, Forensics, Streaming media, Anomaly detection, Benchmark testing BibRef

Carlini, N., Farid, H.,
Evading Deepfake-Image Detectors with White- and Black-Box Attacks,
WMF20(2804-2813)
IEEE DOI 2008
Forensics, Generators, Perturbation methods, Training, Twitter, Robustness, Optimization BibRef

Agarwal, S.[Shruti], Farid, H.[Hany],
Detecting Deep-Fake Videos from Aural and Oral Dynamics,
WMF21(981-989)
IEEE DOI 2109
Irrigation, Shape, Tracking, Face recognition, Forensics, Dynamics, Mouth BibRef

Agarwal, S.[Shruti], Farid, H.[Hany], Fried, O., Agrawala, M.,
Detecting Deep-Fake Videos from Phoneme-Viseme Mismatches,
WMF20(2814-2822)
IEEE DOI 2008
Videos, Lips, Face, Shape, Teeth, Robustness BibRef

Guarnera, L., Giudice, O., Battiato, S.,
DeepFake Detection by Analyzing Convolutional Traces,
WMF20(2841-2850)
IEEE DOI 2008
Generative adversarial networks, Videos, Computer architecture, Data models, Convolution, Feature extraction BibRef

Montserrat, D.M., Hao, H., Yarlagadda, S.K., Baireddy, S., Shao, R., Horvßth, J., Bartusiak, E., Yang, J., GŘera, D., Zhu, F., Delp, E.J.,
Deepfakes Detection with Automatic Face Weighting,
WMF20(2851-2859)
IEEE DOI 2008
Face, Feature extraction, Streaming media, Recurrent neural networks, Training, Tools, Social network services BibRef

Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.,
Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics,
CVPR20(3204-3213)
IEEE DOI 2008
Videos, Visualization, Image color analysis, Decoding, YouTube, Training, Detection algorithms BibRef

Wang, Y., Dantcheva, A.,
A video is worth more than 1000 lies. Comparing 3DCNN approaches for detecting deepfakes,
FG20(515-519)
IEEE DOI 2102
Videos, Information integrity, Training, Face recognition, Solid modeling, Faces BibRef

Tong, X., Wang, L., Pan, X., Wang, J.G.,
An Overview of Deepfake: The Sword of Damocles in AI,
CVIDL20(265-273)
IEEE DOI 2102
face recognition, feature extraction, image classification, video signal processing, confrontation detection, Variational Auto-Encoder BibRef

Yeh, C., Chen, H., Tsai, S., Wang, S.,
Disrupting Image-Translation-Based DeepFake Algorithms with Adversarial Attacks,
WACVWS20(53-62)
IEEE DOI 2006
Generators, Mathematical model, Hair, Software algorithms, Software, Machine learning BibRef

Matern, F., Riess, C., Stamminger, M.,
Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations,
IVF19(83-92)
IEEE DOI 1902
Face, Visualization, Lighting, Estimation, Geometry BibRef

Jeon, H., Bang, Y., Woo, S.S.,
FakeTalkerDetect: Effective and Practical Realistic Neural Talking Head Detection with a Highly Unbalanced Dataset,
HBU19(1285-1287)
IEEE DOI 2004
face recognition, learning (artificial intelligence), neural nets, video signal processing, FakeTalkerDetect, few shot learning BibRef

He, P., Li, H., Wang, H.,
Detection of Fake Images Via The Ensemble of Deep Representations from Multi Color Spaces,
ICIP19(2299-2303)
IEEE DOI 1910
generative adversarial network, fake image detection, multi color spaces, random forest BibRef

Huh, M.Y.[Min-Young], Liu, A.[Andrew], Owens, A.[Andrew], Efros, A.A.[Alexei A.],
Fighting Fake News: Image Splice Detection via Learned Self-Consistency,
ECCV18(XI: 106-124).
Springer DOI 1810
BibRef

Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Emotions in Face Animation, Video Face Synthesis .


Last update:Sep 1, 2022 at 11:00:56