11.14.4.5.3 Face Forgery

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

Yang, J.C.[Jia-Chen], Zhu, Y.[Yong], Xiao, S.[Shuai], Lan, G.P.[Gui-Peng], Li, Y.[Yang],
A controllable face forgery framework to enrich face-privacy-protection datasets,
IVC(127), 2022, pp. 104566.
Elsevier DOI 2211
Privacy and security, Explainable artificial intelligence, Facial forgery, Generative adversarial network, Data diversity 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

Yuan, Y.[Yike], Fu, X.[Xinghe], Wang, G.[Gaoang], Li, Q.M.[Qi-Ming], Li, X.[Xi],
Forgery-Domain-Supervised Deepfake Detection With Non-Negative Constraint,
SPLetters(29), 2022, pp. 2512-2516.
IEEE DOI 2301
Faces, Forgery, Deepfakes, Task analysis, Feature extraction, Crops, Training, Classifier regularization, deepfake detection, feature integration BibRef

Hua, Y.Y.[Ying-Ying], Shi, R.X.[Rui-Xin], Wang, P.[Pengju], Ge, S.M.[Shi-Ming],
Learning Patch-Channel Correspondence for Interpretable Face Forgery Detection,
IP(32), 2023, pp. 1668-1680.
IEEE DOI 2303
Forgery, Faces, Feature extraction, Deep learning, Decorrelation, Visualization, Task analysis, Face forgery detection, patch-channel correspondence BibRef

Yu, B.Y.[Bing-Yao], Li, X.[Xiu], Li, W.H.[Wan-Hua], Zhou, J.[Jie], Lu, J.W.[Ji-Wen],
Discrepancy-Aware Meta-Learning for Zero-Shot Face Manipulation Detection,
IP(32), 2023, pp. 3759-3773.
IEEE DOI 2307
Faces, Face recognition, Metalearning, Forgery, Task analysis, Adaptation models, Optimization, Face manipulation detection, zero-shot problem BibRef

Qiu, H.[Haonan], Chen, S.[Siyu], Gan, B.[Bei], Wang, K.[Kun], Shi, H.F.[Hua-Feng], Shao, J.[Jing], Liu, Z.W.[Zi-Wei],
Few-shot forgery detection via Guided Adversarial Interpolation,
PR(144), 2023, pp. 109863.
Elsevier DOI 2310
Forgery detection, DeepFake, Few-shot, Face manipulation BibRef

Liu, X.L.[Xiao-Long], Yu, Y.[Yang], Li, X.L.[Xiao-Long], Zhao, Y.[Yao],
Magnifying multimodal forgery clues for Deepfake detection,
SP:IC(118), 2023, pp. 117010.
Elsevier DOI 2310
Deepfake forgery detection, Multimodal clues magnification, Cross-modal inconsistency, Multi-scale representation BibRef

Wu, B.[Bin], Su, L.C.[Li-Chao], Chen, D.[Dan], Cheng, Y.L.[Yong-Li],
FPC-Net: Learning to detect face forgery by adaptive feature fusion of patch correlation with CG-Loss,
IET-CV(17), No. 3, 2023, pp. 330-340.
DOI Link 2305
adaptive feature fusion, facial forgery detection BibRef

Zhu, X.Y.[Xiang-Yu], Fei, H.Y.[Hong-Yan], Zhang, B.[Bin], Zhang, T.S.[Tian-Shuo], Zhang, X.Y.[Xiao-Yu], Li, S.Z.[Stan Z.], Lei, Z.[Zhen],
Face Forgery Detection by 3D Decomposition and Composition Search,
PAMI(45), No. 7, July 2023, pp. 8342-8357.
IEEE DOI 2306
Faces, Forgery, Face recognition, Feature extraction, Lighting, Composition search, differentiable search, fake face, 3D face model BibRef

Zhu, X.Y.[Xiang-Yu], Wang, H.[Hao], Fei, H.Y.[Hong-Yan], Lei, Z.[Zhen], Li, S.Z.[Stan Z.],
Face Forgery Detection by 3D Decomposition,
CVPR21(2928-2938)
IEEE DOI 2111
Geometry, Shape, Face recognition, Lighting, Production BibRef

She, H.M.[Hui-Min], Hu, Y.J.[Yong-Jian], Liu, B.B.[Bei-Bei], Li, J.[Jicheng], Li, C.T.[Chang-Tsun],
Learnable Information-Preserving Image Resizer for Face Forgery Detection,
SPLetters(30), 2023, pp. 1657-1661.
IEEE DOI 2311
BibRef

Wang, Y.[Yukai], Peng, C.L.[Chun-Lei], Liu, D.[Decheng], Wang, N.N.[Nan-Nan], Gao, X.B.[Xin-Bo],
Spatial-Temporal Frequency Forgery Clue for Video Forgery Detection in VIS and NIR Scenario,
CirSysVideo(33), No. 12, December 2023, pp. 7943-7956.
IEEE DOI Code:
WWW Link. 2312
BibRef

Guo, Z.Q.[Zhi-Qing], Yang, G.[Gaobo], Chen, J.[Jiyou], Sun, X.M.[Xing-Ming],
Exposing Deepfake Face Forgeries With Guided Residuals,
MultMed(25), 2023, pp. 8458-8470.
IEEE DOI 2312
BibRef

Fu, Z.X.[Zhi-Xiao], Chen, X.Y.[Xin-Yuan], Liu, D.[Daizong], Qu, X.Y.[Xiao-Ye], Dong, J.F.[Jian-Feng], Zhang, X.H.[Xu-Hong], Ji, S.[Shouling],
Multi-level feature disentanglement network for cross-dataset face forgery detection,
IVC(135), 2023, pp. 104686.
Elsevier DOI 2306
Face forgery detection, Cross-dataset evaluation, Multi-level representation, Feature disentangling, Adversarial learning BibRef

Xiao, S.[Shuai], Lan, G.[Guipeng], Yang, J.C.[Jia-Chen], Lu, W.[Wen], Meng, Q.G.[Qing-Gang], Gao, X.B.[Xin-Bo],
MCS-GAN: A Different Understanding for Generalization of Deep Forgery Detection,
MultMed(26), 2024, pp. 1333-1345.
IEEE DOI 2402
Forgery, Faces, Deepfakes, Anomaly detection, Generative adversarial networks, Data models, Feature extraction, image reconstruction BibRef

Guo, Z.Q.[Zhi-Qing], Wang, L.[Liejun], Yang, W.Z.[Wen-Zhong], Yang, G.[Gaobo], Li, K.Q.[Ke-Qin],
LDFnet: Lightweight Dynamic Fusion Network for Face Forgery Detection by Integrating Local Artifacts and Global Texture Information,
CirSysVideo(34), No. 2, February 2024, pp. 1255-1265.
IEEE DOI 2402
Forgery, Faces, Feature extraction, Face recognition, Visualization, Magnetic heads, Computational modeling, Face forgery detection, dynamic fusion BibRef

Liu, D.[Decheng], Zheng, Z.[Zeyang], Peng, C.L.[Chun-Lei], Wang, Y.[Yukai], Wang, N.N.[Nan-Nan], Gao, X.B.[Xin-Bo],
Hierarchical Forgery Classifier on Multi-Modality Face Forgery Clues,
MultMed(26), 2024, pp. 2894-2905.
IEEE DOI 2402
Forgery, Faces, Face recognition, Feature extraction, Videos, Task analysis, Frequency-domain analysis, Face forgery detection, hierarchical classifier BibRef

Yu, Y.[Yang], Ni, R.R.[Rong-Rong], Yang, S.Y.[Si-Yuan], Zhao, Y.[Yao], Kot, A.C.[Alex C.],
Narrowing Domain Gaps With Bridging Samples for Generalized Face Forgery Detection,
MultMed(26), 2024, pp. 3405-3417.
IEEE DOI 2402
Faces, Forgery, Feature extraction, Finite element analysis, Generative adversarial networks, Nickel, cross-domain alignment BibRef

Ghosh, T.[Tanusree], Naskar, R.[Ruchira],
Less is more: A minimalist approach to robust GAN-generated face detection,
PRL(179), 2024, pp. 185-191.
Elsevier DOI 2403
Fake image detection, Deepfake detection, GAN forensics, Digital image forensics, Synthetic image detection, GAN-face detection BibRef

N, A.R.P.[Aravinda Reddy P], Ramachandra, R.[Raghavendra], Rao, K.S.[Krothapalli Sreenivasa], Mitra, P.[Pabitra],
MLSD-GAN: Generating Strong High Quality Face Morphing Attacks Using Latent Semantic Disentanglement,
ICCVMI23(1-6)
IEEE DOI 2403
Training, Interpolation, Face recognition, Semantics, Machine intelligence, Biometrics, Face recognition, bStyleGAN BibRef

Huang, J.J.[Jia-Jun], Du, C.B.[Cheng-Bin], Zhu, X.[Xinqi], Ma, S.Q.[Si-Qi], Nepal, S.[Surya], Xu, C.[Chang],
Anti-Compression Contrastive Facial Forgery Detection,
MultMed(26), 2024, pp. 6166-6177.
IEEE DOI 2404
Forgery, Image coding, Deepfakes, Data models, Faces, Training, Feature extraction, Facial forgery detection, anti-compression BibRef

Bai, W.M.[Wei-Ming], Liu, Y.F.[Yu-Fan], Zhang, Z.P.[Zhi-Peng], Li, B.[Bing], Hu, W.M.[Wei-Ming],
AUNet: Learning Relations Between Action Units for Face Forgery Detection,
CVPR23(24709-24719)
IEEE DOI 2309
BibRef

Shi, L.[Liang], Zhang, J.[Jie], Liang, C.Y.[Chen-Yue], Shan, S.G.[Shi-Guang],
Unknown Aware Feature Learning for Face Forgery Detection,
FG21(1-5)
IEEE DOI 2303
Representation learning, Face recognition, Predictive models, Benchmark testing, Forgery BibRef

Zhu, Y.Z.[Yi-Zhe], Gao, J.L.[Jia-Lin], Liu, Q.[Qiong], Zhou, X.[Xi],
Attention-guided Fine-grained Feature Learning For Robust Face Forgery Detection,
ICPR22(1222-1228)
IEEE DOI 2212
Representation learning, Face recognition, Frequency-domain analysis, Perturbation methods, Semantics, Streaming media BibRef

Zhuang, W.[Wanyi], Chu, Q.[Qi], Tan, Z.T.[Zhen-Tao], Liu, Q.K.[Qian-Kun], Yuan, H.J.[Hao-Jie], Miao, C.T.[Chang-Tao], Luo, Z.X.[Zi-Xiang], Yu, N.H.[Neng-Hai],
UIA-ViT: Unsupervised Inconsistency-Aware Method Based on Vision Transformer for Face Forgery Detection,
ECCV22(V:391-407).
Springer DOI 2211
BibRef

Sun, K.[Ke], Liu, H.[Hong], Yao, T.P.[Tai-Ping], Sun, X.S.[Xiao-Shuai], Chen, S.[Shen], Ding, S.H.[Shou-Hong], Ji, R.R.[Rong-Rong],
An Information Theoretic Approach for Attention-Driven Face Forgery Detection,
ECCV22(XIV:111-127).
Springer DOI 2211
BibRef

Liang, J.H.[Jia-Hao], Shi, H.F.[Hua-Feng], Deng, W.H.[Wei-Hong],
Exploring Disentangled Content Information for Face Forgery Detection,
ECCV22(XIV:128-145).
Springer DOI 2211
BibRef

Song, L.[Luchuan], Fang, Z.[Zheng], Li, X.D.[Xiao-Dan], Dong, X.Y.[Xiao-Yi], Jin, Z.C.[Zhen-Chao], Chen, Y.F.[Yue-Feng], Lyu, S.W.[Si-Wei],
Adaptive Face Forgery Detection in Cross Domain,
ECCV22(XXXIV:467-484).
Springer DOI 2211
BibRef

Ni, Y.S.[Yun-Sheng], Meng, D.[Depu], Yu, C.Q.[Chang-Qian], Quan, C.B.[Cheng-Bin], Ren, D.C.[Dong-Chun], Zhao, Y.J.[You-Jian],
CORE: Consistent Representation Learning for Face Forgery Detection,
WMF22(12-21)
IEEE DOI 2210
Representation learning, Codes, Face recognition, Market research, Forgery BibRef

Cao, J.[Junyi], Ma, C.[Chao], Yao, T.P.[Tai-Ping], Chen, S.[Shen], Ding, S.H.[Shou-Hong], Yang, X.K.[Xiao-Kang],
End-to-End Reconstruction-Classification Learning for Face Forgery Detection,
CVPR22(4103-4112)
IEEE DOI 2210
Training, Visualization, Face recognition, Benchmark testing, Forgery, Cognition, Robustness, Face and gestures, Biometrics BibRef

Jia, S.[Shuai], Ma, C.[Chao], Yao, T.P.[Tai-Ping], Yin, B.[Bangjie], Ding, S.H.[Shou-Hong], Yang, X.K.[Xiao-Kang],
Exploring Frequency Adversarial Attacks for Face Forgery Detection,
CVPR22(4093-4102)
IEEE DOI 2210
Visualization, Face recognition, Frequency-domain analysis, Perturbation methods, Computational modeling, Detectors, Face and gestures BibRef

Fei, J.W.[Jian-Wei], Dai, Y.S.[Yun-Shu], Yu, P.P.[Pei-Peng], Shen, T.R.[Tian-Run], Xia, Z.H.[Zhi-Hua], Weng, J.[Jian],
Learning Second Order Local Anomaly for General Face Forgery Detection,
CVPR22(20238-20248)
IEEE DOI 2210
Representation learning, Adaptation models, Annotations, Face recognition, Forgery, Filtering theory, Biometrics, Representation learning BibRef

Liu, J.[Jie], Wang, J.J.[Jing-Jing], Zhang, P.[Peng], Wang, C.[Chunmao], Xie, D.[Di], Pu, S.L.[Shi-Liang],
Multi-scale Wavelet Transformer for Face Forgery Detection,
ACCV22(VI:52-68).
Springer DOI 2307
BibRef

Yang, P.[Puning], Huang, H.B.[Huai-Bo], Wang, Z.Y.[Zhi-Yong], Yu, A.[Aijing], He, R.[Ran],
Confidence-calibrated Face Image Forgery Detection with Contrastive Representation Distillation,
ACCV22(IV:3-19).
Springer DOI 2307
BibRef

Chen, H.[Han], Lin, Y.Z.[Yu-Zhen], Li, B.[Bin],
Exposing Face Forgery Clues via Retinex-based Image Enhancement,
ACCV22(IV:20-34).
Springer DOI 2307
BibRef

Sun, Y.Y.[Yu-Yang], Zhang, Z.Y.[Zhi-Yong], Echizen, I.[Isao], Nguyen, H.H.[Huy H.], Qiu, C.Z.[Chang-Zhen], Sun, L.[Lu],
Face Forgery Detection Based on Facial Region Displacement Trajectory Series,
BioAttack23(633-642)
IEEE DOI 2302
Deepfakes, Law, Time series analysis, Logic gates, Media, Forgery, Trajectory BibRef

Wu, H.T.[Hao-Tian], Wang, P.[Peipei], Wang, X.[Xin], Xiang, J.[Ji], Gong, R.[Rui],
GGViT: Multistream Vision Transformer Network in Face2Face Facial Reenactment Detection,
ICPR22(2335-2341)
IEEE DOI 2212
Image quality, Image coding, Social networking (online), Network architecture, Transformers, Forgery BibRef

Lin, Y.Z.[Yu-Zhen], Chen, H.[Han], Li, B.[Bin], Wu, J.Q.[Jun-Qiang],
Towards Generalizable DEEPFAKE Face Forgery Detection with Semi-Supervised Learning and Knowledge Distillation,
ICIP22(576-580)
IEEE DOI 2211
Training, Deepfakes, Semisupervised learning, Benchmark testing, Feature extraction, Forgery, Data models, Deepfake detection, knowledge distillation BibRef

Haliassos, A.[Alexandros], Mira, R.[Rodrigo], Petridis, S.[Stavros], Pantic, M.[Maja],
Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detection,
CVPR22(14930-14942)
IEEE DOI 2210
Training, Visualization, Detectors, Pressing, Forgery, Robustness, Pattern recognition, Computer vision for social good, Self- semi- meta- Video analysis and understanding 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

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

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

Luo, Y.C.[Yu-Chen], Zhang, Y.[Yong], Yan, J.C.[Jun-Chi], Liu, W.[Wei],
Generalizing Face Forgery Detection with High-frequency Features,
CVPR21(16312-16321)
IEEE DOI 2111
Training, Correlation, Image color analysis, Databases, Face recognition, Detectors, Feature extraction BibRef

Li, J.M.[Jia-Ming], Xie, H.T.[Hong-Tao], Li, J.H.[Jia-Hong], Wang, Z.Y.[Zhong-Yuan], Zhang, Y.D.[Yong-Dong],
Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection,
CVPR21(6454-6463)
IEEE DOI 2111
Measurement, Face recognition, Frequency-domain analysis, Filter banks, Boosting, Forgery BibRef

Zhou, T.F.[Tian-Fei], Wang, W.G.[Wen-Guan], Liang, Z.Y.[Zhi-Yuan], Shen, J.B.[Jian-Bing],
Face Forensics in the Wild,
CVPR21(5774-5784)
IEEE DOI 2111
Location awareness, Costs, Face recognition, Forensics, Benchmark testing, Forgery, Classification algorithms BibRef

He, Y.N.[Yi-Nan], Gan, B.[Bei], Chen, S.[Siyu], Zhou, Y.C.[Yi-Chun], Yin, G.J.[Guo-Jun], Song, L.C.[Lu-Chuan], Sheng, L.[Lu], Shao, J.[Jing], Liu, Z.W.[Zi-Wei],
ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis,
CVPR21(4358-4367)
IEEE DOI 2111
Location awareness, Image segmentation, Technological innovation, Annotations, Face recognition, Perturbation methods, Benchmark testing BibRef

Liu, H.G.[Hong-Gu], Li, X.D.[Xiao-Dan], Zhou, W.B.[Wen-Bo], Chen, Y.F.[Yue-Feng], He, Y.[Yuan], Xue, H.[Hui], Zhang, W.M.[Wei-Ming], Yu, N.H.[Neng-Hai],
Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain,
CVPR21(772-781)
IEEE DOI 2111
Face recognition, Frequency-domain analysis, Semantics, Forgery, Robustness, Security BibRef

Wang, C.R.[Cheng-Rui], Deng, W.H.[Wei-Hong],
Representative Forgery Mining for Fake Face Detection,
CVPR21(14918-14927)
IEEE DOI 2111
Codes, Face recognition, Refining, Training data, Data visualization, Detectors, Forgery BibRef

Schwarcz, S.[Steven], Chellappa, R.[Rama],
Finding Facial Forgery Artifacts with Parts-Based Detectors,
WMF21(933-942)
IEEE DOI 2109
Deep learning, Social networking (online), Face recognition, Neural networks, Detectors BibRef

Han, J.[Jian], Gevers, T.[Theo],
MMD Based Discriminative Learning for Face Forgery Detection,
ACCV20(V:121-136).
Springer DOI 2103
BibRef

Tarasiou, M., Zafeiriou, S.P.,
Extracting Deep Local Features to Detect Manipulated Images of Human Faces,
ICIP20(1821-1825)
IEEE DOI 2011
Faces, Feature extraction, Training, Forgery, Task analysis, Videos, Image segmentation BibRef

Huang, R., Fang, F., Nguyen, H.H., Yamagishi, J., Echizen, I.,
Security of Facial Forensics Models Against Adversarial Attacks,
ICIP20(2236-2240)
IEEE DOI 2011
Perturbation methods, Image segmentation, Neurons, Linear programming, Security, Forensics, Forgery, forgery forensics, over-firing BibRef

Li, L., Bao, J., Yang, H., Chen, D., Wen, F.,
Advancing High Fidelity Identity Swapping for Forgery Detection,
CVPR20(5073-5082)
IEEE DOI 2008
Face, Lighting, Image resolution, Adaptive systems, Shape, Generators, Training BibRef

Hulzebosch, N., Ibrahimi, S., Worring, M.,
Detecting CNN-Generated Facial Images in Real-World Scenarios,
WMF20(2729-2738)
IEEE DOI 2008
Training, Image color analysis, Data models, Visualization, Forgery, Image resolution BibRef

Jiang, L., Li, R., Wu, W., Qian, C., Loy, C.C.,
DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection,
CVPR20(2886-2895)
IEEE DOI 2008
Videos, Face, Forgery, Benchmark testing, Perturbation methods, Data collection, Lighting BibRef

Wang, Z.D.[Zhen-Dong], Bao, J.M.[Jian-Min], Zhou, W.G.[Wen-Gang], Wang, W.L.[Wei-Lun], Li, H.Q.[Hou-Qiang],
AltFreezing for More General Video Face Forgery Detection,
CVPR23(4129-4138)
IEEE DOI 2309
BibRef

Zheng, Y.L.[Ying-Lin], Bao, J.M.[Jian-Min], Chen, D.[Dong], Zeng, M.[Ming], Wen, F.[Fang],
Exploring Temporal Coherence for More General Video Face Forgery Detection,
ICCV21(15024-15034)
IEEE DOI 2203
Convolution, Coherence, Transformers, Feature extraction, Forgery, Robustness, Kernel, Video analysis and understanding BibRef

Li, L.Z.[Ling-Zhi], Bao, J.M.[Jian-Min], Zhang, T.[Ting], Yang, H.[Hao], Chen, D.[Dong], Wen, F.[Fang], Guo, B.N.[Bai-Ning],
Face X-Ray for More General Face Forgery Detection,
CVPR20(5000-5009)
IEEE DOI 2008
Face, Forgery, X-ray imaging, Image color analysis, Detectors, Forensics, Focusing BibRef

Dang, H., Liu, F., Stehouwer, J., Liu, X., Jain, A.K.,
On the Detection of Digital Face Manipulation,
CVPR20(5780-5789)
IEEE DOI 2008
Face, Forgery, Videos, Machine learning, Cameras 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 .