Implicit identity leakage: The stumbling block to improving deepfake detection generalization
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In this paper, we analyse the generalization ability of binary classifiers for the task of deepfake detection. We find that the stumbling block to their generalization is caused by the unexpected learned identity representation on images. Termed as the Implicit Identity Leakage, this phenomenon has been qualitatively and quantitatively verified among various DNNs. Furthermore, based on such understanding, we propose a simple yet effective method named the ID-unaware Deepfake Detection Model to reduce the influence of this phenomenon. Extensive experimental results demonstrate that our method outperforms the state-of-the-art in both in-dataset and cross-dataset evaluation. The code is available at https://github.com/megvii-research/CADDM.
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Recent methods still face challenges, often learning dataset-specific cues that fail to detect unseen forgery samples.
“Despite these efforts, recent methods still face challenges, often learning dataset-specific cues that fail to detect unseen forgery samples.”
From Towards More General Video-based Deepfake Detection through Facial Component Guided Adaptation for Foundation Model (2024) · cites this paper as [9]
Using Adam as the optimizer is a common setting, and a substantial number of deepfake detection methods in existing literature have adopted this configuration.
“Using Adam as the optimizer can be considered a common setting, and a substantial number of deepfake detection methods in existing literature have adopted this configuration (paper [20, 27, 26, 3, 12]).”
From DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection (2023) · cites this paper as [12]
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892 · see citing works
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
(2016) · Cited by 31,590 · see citing works
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105 · see citing works
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,100 · see citing works
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380 · see citing works
YOLO9000: Better, Faster, Stronger
Redmon, J., & Farhadi, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.690 · Cited by 19,892 · see citing works
YOLOv3: An Incremental Improvement
Redmon, J., & Farhadi, A. (2018) · arXiv · doi:10.48550/arxiv.1804.02767 · Cited by 17,141 · see citing works
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Tan, M., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1905.11946 · Cited by 17,072 · see citing works
Momentum Contrast for Unsupervised Visual Representation Learning
He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00975 · Cited by 13,347 · see citing works
Self-citationA Style-Based Generator Architecture for Generative Adversarial Networks
Karras, T., Laine, S., & Aila, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00453 · Cited by 11,714 · see citing works
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020) · arXiv · doi:10.48550/arxiv.2002.05709 · Cited by 10,857 · see citing works
Analyzing and Improving the Image Quality of StyleGAN
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., & Aila, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00813 · Cited by 6,332 · see citing works
Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks
Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. (2016) · IEEE Signal Processing Letters · doi:10.1109/lsp.2016.2603342 · Cited by 6,214 · see citing works
Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments
Huang, G. B., Mattar, M., Berg, T. L., & Miller, E. G. (2008) · HAL · Cited by 4,445 · see citing works
FaceForensics++: Learning to Detect Manipulated Facial Images
Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Niessner, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00009 · Cited by 3,314 · see citing works
Pérez, P., Gangnet, M., & Blake, A. (2003) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/1201775.882269 · Cited by 2,041 · see citing works
Receptive Field Block Net for Accurate and Fast Object Detection
Liu, S., Huang, D., & Wang, Y. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01252-6_24 · Cited by 1,885 · see citing works
MesoNet: a Compact Facial Video Forgery Detection Network
Afchar, D., Nozick, V., Yamagishi, J., & Echizen, I. (2018) · 2018 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs.2018.8630761 · Cited by 1,872 · see citing works
Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics
Li, Y., Yang, X., Sun, P., Qi, H., & Lyu, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00327 · Cited by 1,846 · cited 3× · see citing works
“ages, which are usually not available in public deepfake datasets [36, 56].”
BackgroundMethodStarGAN v2: Diverse Image Synthesis for Multiple Domains
Choi, Y., Uh, Y., Yoo, J., & Ha, J.-W. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00821 · Cited by 1,663 · see citing works
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
Luo, W., Li, Y., Urtasun, R., & Zemel, R. (2017) · arXiv · doi:10.48550/arxiv.1701.04128 · Cited by 1,575 · see citing works
Thies, J., Zollhöfer, M., & Nießner, M. (2019) · ACM Transactions on Graphics · doi:10.1145/3306346.3323035 · Cited by 1,410 · see citing works
Scale-Aware Trident Networks for Object Detection
Li, Y., Chen, Y., Wang, N., & Zhang, Z. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00615 · Cited by 1,225 · see citing works
Face X-Ray for More General Face Forgery Detection
Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., & Guo, B. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00505 · Cited by 1,211 · see citing works
Exposing Deep Fakes Using Inconsistent Head Poses
Yang, X., Li, Y., & Lyu, S. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8683164 · Cited by 1,196 · see citing works
In Ictu Oculi: Exposing AI Created Fake Videos by Detecting Eye Blinking
Li, Y., Chang, M.-C., & Lyu, S. (2018) · 2018 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs.2018.8630787 · Cited by 1,089 · see citing works
Thinking in Frequency: Face Forgery Detection by Mining Frequency-Aware Clues
Qian, Y., Yin, G., Sheng, L., Chen, Z., & Shao, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58610-2_6 · Cited by 1,058 · see citing works
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Zhao, H., Wei, T., Zhou, W., Zhang, W., Chen, D., & Yu, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00222 · Cited by 1,019 · cited 3× · see citing works
“Though plenty of researchers have designed effective methods [32, 74, 75] to”
MethodA Deep Learning Approach to Universal Image Manipulation Detection Using a New Convolutional Layer
Bayar, B., & Stamm, M. C. (2016) · Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security · doi:10.1145/2909827.2930786 · Cited by 838 · see citing works
Exposing DeepFake Videos By Detecting Face Warping Artifacts
Li, Y., & Lyu, S. (2018) · arXiv · doi:10.48550/arxiv.1811.00656 · Cited by 785 · see citing works
S^3FD: Single Shot Scale-Invariant Face Detector
Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., & Li, S. Z. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.30 · Cited by 662 · see citing works
Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain
Liu, H., Li, X., Zhou, W., Chen, Y., He, Y., Xue, H., Zhang, W., & Yu, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00083 · Cited by 600 · see citing works
Thies, J., Zollhöfer, M., Stamminger, M., Theobalt, C., & Nießner, M. (2016) · ACM SIGGRAPH Emerging Technologies · doi:10.1145/2929464.2929475 · Cited by 585 · see citing works
Multi-task Learning for Detecting and Segmenting Manipulated Facial Images and Videos
Nguyen, H. H., Fang, F., Yamagishi, J., & Echizen, I. (2019) · 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS) · doi:10.1109/btas46853.2019.9185974 · Cited by 549 · see citing works
Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection
Haliassos, A., Vougioukas, K., Petridis, S., & Pantic, M. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00500 · Cited by 537 · see citing works
Detecting Deepfakes with Self-Blended Images
Shiohara, K., & Yamasaki, T. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01816 · Cited by 503 · see citing works
Two-Branch Recurrent Network for Isolating Deepfakes in Videos
Masi, I., Killekar, A., Mascarenhas, R. M., Gurudatt, S. P., & AbdAlmageed, W. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58571-6_39 · Cited by 471 · see citing works
SimSwap: An Efficient Framework For High Fidelity Face Swapping
Chen, R., Chen, X., Ni, B., & Ge, Y. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413630 · Cited by 418 · see citing works
Detection of GAN-Generated Fake Images over Social Networks
Marra, F., Gragnaniello, D., Cozzolino, D., & Verdoliva, L. (2018) · 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr.2018.00084 · Cited by 399 · see citing works
STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing
Liu, M., Ding, Y., Xia, M., Liu, X., Ding, E., Zuo, W., & Wen, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00379 · Cited by 394 · see citing works
Learning Self-Consistency for Deepfake Detection
Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., & Xia, W. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01475 · Cited by 383 · see citing works
M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection
Wang, J., Wu, Z., Ouyang, W., Han, X., Chen, J., Jiang, Y.-G., & Li, S.-N. (2022) · Proceedings of the 2022 International Conference on Multimedia Retrieval · doi:10.1145/3512527.3531415 · Cited by 368 · see citing works
Self-citationLi, J., Xie, H., Li, J., Wang, Z., & Zhang, Y. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00639 · Cited by 357 · see citing works
Cozzolino, D., Poggi, G., & Verdoliva, L. (2017) · Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security · doi:10.1145/3082031.3083247 · Cited by 349 · see citing works
Distinguishing computer graphics from natural images using convolution neural networks
Rahmouni, N., Nozick, V., Yamagishi, J., & Echizen, I. (2017) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2017.8267647 · Cited by 335 · see citing works
Local Relation Learning for Face Forgery Detection
Chen, S., Yao, T., Chen, Y., Ding, S., Li, J., & Ji, R. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16193 · Cited by 334 · see citing works
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Nirkin, Y., Wolf, L., Keller, Y., & Hassner, T. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3093446 · Cited by 312 · see citing works
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Chen, L., Zhang, Y., Song, Y., Liu, L., & Wang, J. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01815 · Cited by 307 · see citing works
Self-citationFaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
Li, L., Bao, J., Yang, H., Chen, D., & Wen, F. (2019) · arXiv · doi:10.48550/arxiv.1912.13457 · Cited by 236 · see citing works
(2020) · arXiv (Cornell University) · Cited by 204 · see citing works
On the Generalization of GAN Image Forensics
Xuan, X., Peng, B., Wang, W., & Dong, J. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-31456-9_15 · Cited by 184 · see citing works
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Durall, R., Keuper, M., Pfreundt, F.-J., & Keuper, J. (2019) · arXiv · doi:10.48550/arxiv.1911.00686 · Cited by 184 · see citing works
Fake Faces Identification via Convolutional Neural Network
Mo, H., Chen, B., & Luo, W. (2018) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/3206004.3206009 · Cited by 183 · see citing works
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Dong, X., Bao, J., Chen, D., Zhang, T., Zhang, W., Yu, N., Chen, D., Wen, F., & Guo, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00925 · Cited by 181 · see citing works
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Detecting Both Machine and Human Created Fake Face Images In the Wild
Tariq, S., Lee, S., Kim, H., Shin, Y., & Woo, S. S. (2018) · doi:10.1145/3267357.3267367 · Cited by 177 · see citing works
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Nguyen, H. H., Yamagishi, J., & Echizen, I. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.12467 · Cited by 163 · see citing works
Learning to Detect Fake Face Images in the Wild
Hsu, C.-C., Lee, C.-Y., & Zhuang, Y.-X. (2018) · 2018 International Symposium on Computer, Consumer and Control (IS3C) · doi:10.1109/is3c.2018.00104 · Cited by 148 · see citing works
Distinguishing Between Natural and Computer-Generated Images Using Convolutional Neural Networks
Quan, W., Wang, K., Dong-ming Yan, A., & Zhang, X. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2834147 · Cited by 122 · see citing works
DeepFakesON-Phys: DeepFakes Detection based on Heart Rate EstimationPreprint
Hernandez‐Ortega, J., Tolosana, R., Fiérrez, J., & Morales, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.00400 · Cited by 98 · see citing works
Training Strategies and Data Augmentations in CNN-based DeepFake Video Detection
Bondi, L., Cannas, E. D., Bestagini, P., & Tubaro, S. (2020) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs49906.2020.9360901 · Cited by 91 · see citing works
Swapped face detection using deep learning and subjective assessment
Ding, X., Raziei, Z., Larson, E. C., Olinick, E. V., Krueger, P., & Hahsler, M. (2020) · EURASIP Journal on Information Security · doi:10.1186/s13635-020-00109-8 · Cited by 71 · see citing works
Interpreting Multivariate Shapley Interactions in DNNs
Zhang, H., Xie, Y., Zheng, L., Zhang, D., & Zhang, Q. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i12.17299 · Cited by 45 · see citing works
Adaptive Face Forgery Detection in Cross Domain
Song, L., Fang, Z., Li, X., Dong, X., Jin, Z., Chen, Y., & Lyu, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19830-4_27 · Cited by 35 · see citing works
Towards Generalizable Forgery Detection with Locality-aware AutoEncoder.Preprint
Du, M., Pentyala, S., Li, Y., & Hu, X. (2019) · arXiv (Cornell University) · Cited by 33 · see citing works
Face Forgery Detection via Symmetric Transformer
Song, L., Li, X., Fang, Z., Jin, Z., Chen, Y., & Xu, C. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3547806 · Cited by 21 · see citing works
Flagged first, then by citations
3 of the 75 references couldn’t be matched to a known paper.
Research works printed but not linked: 18
- FirstName Alpher. Frobnication. IEEE TPAMI, 12(1):234- 778, 2002.
- FirstName Alpher and FirstName Fotheringham-Smythe. Frobnication revisited. Journal of Foo, 13(1):234-778, 2003.
- FirstName Alpher, FirstName Fotheringham-Smythe, and FirstName Gamow. Can a machine frobnicate? Journal of Foo, 14(1):234-778, 2004.
- FirstName Alpher and FirstName Gamow. Can a computer frobnicate? In CVPR, pages 234-778, 2005.
- Davide Cozzolino, Andreas Rössler, Justus Thies, Matthias Nießner, and Luisa Verdoliva. Id-reveal: Identity-aware deepfake video detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15108- 15117, 2021.
- Shichao Dong, Jin Wang, Jiajun Liang, Haoqiang Fan, and Renhe Ji. Explaining deepfake detection by analysing image matching. arXiv preprint arXiv:2207.09679, 2022. · arXiv
- Ross Girshick. Fast r-cnn. In Proceedings of the IEEE inter- national conference on computer vision, pages 1440-1448, 2015. 5, 8
- Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kil- ian Q. Weinberger. Densely connected convolutional net- works. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, pages 2261-2269. IEEE Computer Society, 2017.
- Falko Matern, C. Riess, and M. Stamminger. Exploiting vi- sual artifacts to expose deepfakes and face manipulations. 2019 IEEE Winter Applications of Computer Vision Work- shops (WACVW), pages 83-92, 2019.
- Yuyang Qian, Guojun Yin, Lu Sheng, Zixuan Chen, and Jing Shao. Thinking in frequency: Face forgery detection by min- ing frequency-aware clues. In ECCV, 2020.
- Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE in- ternational conference on computer vision, pages 618-626, 2017.
- Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang. Deep high-resolution representation learning for human pose es- timation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5693- 5703, 2019.
- Justus Thies, Michael Zollhöfer, and Matthias Nießner. De- ferred neural rendering: Image synthesis using neural tex- tures. ACM Transactions on Graphics (TOG), 38(4):1-12, 2019. 5
- Justus Thies, Michael Zollhofer, Marc Stamminger, Chris- tian Theobalt, and Matthias Nießner. Face2face: Real-time face capture and reenactment of rgb videos. In Proceed- ings of the IEEE conference on computer vision and pattern recognition, pages 2387-2395, 2016. 1, 5
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszko- reit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett, editors, Advances in Neural Information Processing Systems 30: An- nual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 5998-6008, 2017.
- Shifeng Zhang, Xiangyu Zhu, Zhen Lei, Hailin Shi, Xiaobo Wang, and Stan Z Li. S3fd: Single shot scale-invariant face detector. In Proceedings of the IEEE international confer- ence on computer vision, pages 192-201, 2017. 4
- Hanqing Zhao, Wenbo Zhou, Dongdong Chen, Tianyi Wei, Weiming Zhang, and Nenghai Yu. Multi-attentional deep- fake detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2185- 2194, 2021. 1, 3, 7
- Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning deep features for discrimina- tive localization. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2921-2929, 2016.
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Cui, X., Li, Y., Luo, A., Zhou, J., & Dong, J. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01789 · Cited by 58 · see citing works
FKA-Owl: Advancing Multimodal Fake News Detection through Knowledge-Augmented LVLMs
Liu, X., Li, P., Huang, H., Li, Z., Cui, X., Liang, J., Qin, L., Deng, W., & He, Z. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681089 · Cited by 61 · see citing works
Self-citationA Review of Deepfake Techniques: Architecture, Detection and Datasets
Edwards, P., Nebel, J.-C., Greenhill, D., & Liang, X. (2024) · IEEE Access · doi:10.1109/access.2024.3477257 · Cited by 50 · see citing works
DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection
Yan, Z., Zhang, Y., Yuan, X., Lyu, S., & Wu, B. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-0201 · Cited by 98 · see citing works
Gao, J., Micheletto, M., Orrù, G., Concas, S., Feng, X., Marcialis, G. L., & Roli, F. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108450 · Cited by 52 · see citing works
Improving Deepfake Detection Generalization by Invariant Risk Minimization
Yin, Z., Wang, J., Xiao, Y., Zhao, H., Li, T., Zhou, W., Liu, A., & Liu, X. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3355651 · Cited by 46 · see citing works
Self-citationMoE-FFD: Mixture of Experts for Generalized and Parameter-Efficient Face Forgery Detection
Kong, C., Luo, A., Bao, P., Yu, Y., Li, H., Zheng, Z., Wang, S., & Kot, A. C. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3604443 · Cited by 62 · see citing works
Learning to Discover Forgery Cues for Face Forgery Detection
Tian, J., Chen, P., Cai, Y., Fu, X., Wang, X., Dai, J., & Han, J. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3372773 · Cited by 42 · see citing works
Video and Audio Deepfake Datasets and Open Issues in Deepfake Technology: Being Ahead of the Curve
Akhtar, Z., Pendyala, T. L., & Athmakuri, V. S. (2024) · Forensic Sciences · doi:10.3390/forensicsci4030021 · Cited by 39 · see citing works
Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detector
Guo, X., Song, X., Zhang, Y., Liu, X., & Liu, X. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52734.2025.00019 · Cited by 34 · see citing works
Deep Fake Image Detection using Xception Architecture
Joshi, P., & Nivethitha, V. (2024) · International Conference on Recent Trends in Computer Science and Technology (ICRTCST) · doi:10.1109/icrtcst61793.2024.10578398 · Cited by 32 · see citing works
Casu, M., Guarnera, L., Caponnetto, P., & Battiato, S. (2024) · Forensic Science International: Digital Investigation · doi:10.1016/j.fsidi.2024.301795 · Cited by 30 · see citing works
Han, Y.-H., Huang, T.-M., Hua, K.-L., & Chen, J.-C. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52734.2025.02141 · Cited by 30 · see citing works
Distilled transformers with locally enhanced global representations for face forgery detection
Zhang, Y., Li, Q., Yu, Z., & Shen, L. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111253 · Cited by 32 · see citing works
LampMark: Proactive Deepfake Detection via Training-Free Landmark Perceptual Watermarks
Wang, T., Huang, M., Cheng, H. H., Zhang, X., & Shen, Z. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680869 · Cited by 29 · see citing works
Towards General Visual-Linguistic Face Forgery Detection
Sun, K., Chen, S., Yao, T., Zhou, Z., Ji, J., Sun, X., Lin, C., & Ji, R. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01823 · Cited by 37 · see citing works
Self-citationGeneralizable deepfake detection via Spatial Kernel Selection and Halo Attention Network
Guo, S., Li, Q., Gao, M., Zhu, X., & Rida, I. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105582 · Cited by 30 · see citing works
Towards Unified Defense for Face Forgery and Spoofing Attacks via Dual Space Reconstruction Learning
Cao, J., Zhang, K.-Y., Yao, T., Ding, S., Yang, X., & Ma, C. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02151-2 · Cited by 27 · see citing works
FreqDebias: Towards Generalizable Deepfake Detection via Consistency-Driven Frequency Debiasing
Kashiani, H., Talemi, N. A., & Afghah, F. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.00820 · Cited by 29 · see citing works
A Review of Deepfake and Its Detection: From Generative Adversarial Networks to Diffusion Models
Liu, B., Liu, B., Zhu, T., & Ding, M. (2025) · International Journal of Intelligent Systems · doi:10.1155/int/9987535 · Cited by 31 · see citing works
Lin, Y., Song, W., Li, B., Li, Y., Ni, J., Chen, H., & Li, Q. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73016-0_7 · Cited by 23 · see citing works
Deepfake Detection Based on the Adaptive Fusion of Spatial‐Frequency Features
Wang, F., Chen, Q., Jing, B., Tang, Y., Song, Z., & Wang, B. (2024) · International Journal of Intelligent Systems · doi:10.1155/2024/7578036 · Cited by 23 · see citing works
Fine-Grained Open-Set Deepfake Detection via Unsupervised Domain Adaptation
Zhou, X., Han, H., Shan, S., & Chen, X. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3435440 · Cited by 22 · see citing works
D 2 Fusion: Dual-domain fusion with feature superposition for Deepfake detection
Qiu, X., Miao, X., Wan, F., Duan, H., Shah, T., Ojha, V., Long, Y., & Ranjan, R. K. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103087 · Cited by 23 · see citing works
Adversarially Robust Deepfake Detection via Adversarial Feature Similarity Learning
Khan, S. S., Chen, J.-C., Liao, W., & Chen, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-53311-2_37 · Cited by 21 · see citing works
Deepfake Detection Fighting Against Noisy Label Attack
Qiao, T., Xie, S., Chen, Y., Retraint, F., Shi, R., & Luo, X. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3385286 · Cited by 19 · see citing works
MeST-Former: Motion-enhanced Spatiotemporal Transformer for generalizable Deepfake detection
Liu, B., Liu, B., Ding, M., & Zhu, T. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128588 · Cited by 22 · see citing works
Coexistence of Deepfake Defenses: Addressing the Poisoning Challenge
Park, J., Park, L. H., Ahn, H. E., & Kwon, T. (2024) · IEEE Access · doi:10.1109/access.2024.3353785 · Cited by 21 · see citing works
LGDF-Net: Local and Global Feature-Based Dual-Branch Fusion Networks for Deepfake Detection
Long, M., Liu, Z., Zhang, L., & Peng, F. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3530402 · Cited by 18 · see citing works
Kou, Y., Li, P., Ma, H., Zhou, J., Huang, Z. A., & Li, X. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11225-7 · Cited by 17 · see citing works
Leveraging facial landmarks improves generalization ability for deepfake detection
Gao, Q., Zhang, B., Wu, J., Luo, W., Teng, Z., & Jianping Fan, B. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111528 · Cited by 19 · see citing works
Tariq, S., Woo, S. S., Singh, P., Irmalasari, I., Gupta, S., & Gupta, D. (2025) · Proceedings of the 33rd ACM International Conference on Multimedia · doi:10.1145/3746027.3755786 · Cited by 15 · see citing works
SoK: Systematization and Benchmarking of Deepfake Detectors in a Unified Framework
Le, B. M., Kim, J., Woo, S. S., Moore, K., Abuadbba, A., & Tariq, S. (2025) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp63326.2025.00055 · Cited by 23 · see citing works
ED4: Explicit Data-Level Debiasing for Deepfake Detection
Cheng, J., Zhang, Y., Zou, Q., Yan, Z., Liang, C., Wang, Z., & Li, C. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3588323 · Cited by 14 · see citing works
IDCNet: Image Decomposition and Cross-View Distillation for Generalizable Deepfake Detection
Wang, Z., Chen, Y., Yao, Y., Han, M., Xing, W., & Li, M. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3593353 · Cited by 14 · see citing works
Semantics-Oriented Multitask Learning for DeepFake Detection: A Joint Embedding Approach
Zou, M., Yu, B., Zhan, Y., Lyu, S., & Ma, K. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3572508 · Cited by 15 · see citing works
Exploring Bi-Level Inconsistency via Blended Images for Generalizable Face Forgery Detection
Jiang, P., Xie, H., Yu, L., Jin, G., & Zhang, Y. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3417266 · Cited by 13 · see citing works
Inspector for Face Forgery Detection: Defending Against Adversarial Attacks From Coarse to Fine
Xia, R., Zhou, D., Liu, D., Li, J., Yuan, L., Wang, N., & Gao, X. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3434388 · Cited by 13 · see citing works
Customized Transformer Adapter With Frequency Masking for Deepfake Detection
Shi, Z., Chen, H., Jia, Y., Zhang, D., Lu, W., & Yang, X. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3574983 · Cited by 12 · see citing works
From Covert Hiding To Visual Editing: Robust Generative Video Steganography
Mao, X., Hu, X., Peng, W., Gan, Z., Qian, Z., Zhang, X., & Li, S. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681149 · Cited by 12 · see citing works
Identity-Driven Multimedia Forgery Detection via Reference Assistance
Xu, J., Chen, J., Song, X., Han, F., Shan, H., & Jiang, Y. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680622 · Cited by 12 · see citing works
Towards generalizable face forgery detection via mitigating spurious correlation
Bai, N., Wang, X., Han, R., Hou, J., Wang, Q., & Pang, S. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106909 · Cited by 13 · see citing works
Deepfake detection through ensemble learning
Jabra, M. B., Cheikhrouhou, O., & Benamor, A. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20932-w · Cited by 12 · see citing works
Exploring varying color spaces through representative forgery learning to improve deepfake detection
Amin, M. A., Hu, Y., Yu, G., & Amin, M. Z. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104426 · Cited by 12 · see citing works
A Hybrid approach for Deepfake Detection using CNN-RNN
Antad, S. M., Arthamwar, V., Deshmukh, R. K., Chame, A. U., & Chhangani, H. P. (2024) · OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON) · doi:10.1109/otcon60325.2024.10687890 · Cited by 11 · see citing works
Advancing Generalized Deepfake Detector with Forgery Perception Guidance
Xia, R., Zhou, D., Liu, D., Yuan, L., Wang, S., Li, J., Wang, N., & Gao, X. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680713 · Cited by 13 · see citing works
Li, Y., Zhang, Y., Yang, H., Chen, B., & Huang, D. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02091-x · Cited by 10 · see citing works
PolyGlotFake: A Novel Multilingual and Multimodal DeepFake Dataset
Hou, Y., Fu, H., Chen, C., Li, Z., Zhang, H., & Zhao, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78341-8_12 · Cited by 13 · see citing works
Multi-Scale High-Frequency Vision Transformer for Face Forgery Detection
Chen, Z., Li, H., & Hu, J. (2025) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2025.3543692 · Cited by 9 · see citing works
FSD-GAN: Generative Adversarial Training for Face Swap Detection via the Latent Noise Fingerprint
Ge, J., Cao, J., Zhao, Z., & Liu, B. (2025) · Journal of Computer Science and Technology · doi:10.1007/s11390-024-3337-8 · Cited by 11 · see citing works
Spatio-Temporal Catcher: A Self-Supervised Transformer for Deepfake Video Detection
Li, M., Li, X., Yu, K., Deng, C. D., Huang, H., Mao, F., Xue, H., & Li, M. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3613842 · Cited by 11 · see citing works
Building Robust Video-Level Deepfake Detection via Audio-Visual Local-Global Interactions
Wang, Y., Wu, X., Zhang, J., Jing, M., Lu, K., Yu, J., Su, W., Gao, F., Liu, Q., Sun, J., et al. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3688985 · Cited by 9 · see citing works
Self-citationCSTAN: A Deepfake Detection Network with CST Attention for Superior Generalization
Yang, R., You, K., Pang, C., Luo, X., & Lan, R. (2024) · Sensors · doi:10.3390/s24227101 · Cited by 9 · see citing works
Semantic Contextualization of Face Forgery: A New Definition, Dataset, and Detection Method
Zou, M., Yu, B., Zhan, Y., Lyu, S., & Ma, K. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3557667 · Cited by 9 · see citing works
HiDF: A Human-Indistinguishable Deepfake Dataset
Kang, C., Jeong, S., Lee, J., Choi, D., Woo, S. S., & Han, J. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3737399 · Cited by 10 · see citing works
FractalForensics: Proactive Deepfake Detection and Localization via Fractal Watermarks
Wang, T., Cheng, H., Liu, M.-H., & Kankanhalli, M. (2025) · Proceedings of the 33rd ACM International Conference on Multimedia · doi:10.1145/3746027.3754544 · Cited by 9 · see citing works
Beyond Spatial Frequency: Pixel-Wise Temporal Frequency-Based Deepfake Video Detection
Kim, T. H., Choi, J., Jeong, Y., Noh, H., Yoo, J., Baek, S., & Choi, J. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01042 · Cited by 11 · see citing works
Adversarial Robust Safeguard for Evading Deep Facial Manipulation
Guan, J., Zhao, Y., Xu, Z., Meng, C., Xu, K., & Zhao, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i1.27762 · Cited by 8 · see citing works
A Case Study on how Beautification Filters Can Fool Deepfake Detectors
Libourel, A., Husseini, S., Mirabet-Herranz, N., & Dugelay, J. (2024) · International Workshop on Biometrics and Forensics (IWBF) · doi:10.1109/iwbf62628.2024.10593932 · Cited by 8 · see citing works
Local artifacts amplification for deepfakes augmentation
Peng, C., Sun, F., Liu, D., Wang, N., & Gao, X. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106692 · Cited by 8 · see citing works
Big Brother Is Watching: Proactive Deepfake Detection via Learnable Hidden Face
Li, H., Yang, S., Xia, R., Yuan, L., & Gao, X. (2025) · IEEE Signal Processing Letters · doi:10.1109/lsp.2025.3570660 · Cited by 8 · see citing works
Noise-Informed Diffusion-Generated Image Detection With Anomaly Attention
Guan, W., Wang, W., Peng, B., He, Z., Dong, J., & Cheng, H. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3573161 · Cited by 8 · see citing works
HSFF-Net: Hierarchical spectral-feature fusion network for deepfake detection and localization
Han, R., Wang, X., Bai, N., Hou, J., Zhang, W., & Li, J. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107967 · Cited by 10 · see citing works
Enhanced Deep Learning DeepFake Detection Integrating Handcrafted Features
Hinke-Navarro, A., Nieto-Hidalgo, M., Espín, J. M., & Tapia, J. E. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04968-1_6 · Cited by 9 · see citing works
DeepFidelity: Perceptual Forgery Fidelity Assessment for Deepfake Detection
Peng, C., Guo, H., Liu, D., Wang, N., Hu, R., & Gao, X. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3621207 · Cited by 11 · see citing works
Subjective and Objective Quality Assessment of Multi-Attribute Retouched Face Images
Yue, G., Wu, H., Yan, W., Zhou, T., Liu, H., & Zhou, W. (2024) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2024.3374043 · Cited by 8 · see citing works
Gao, Y., Zhang, Y., Zeng, P., & Ma, Y. (2024) · Electronics · doi:10.3390/electronics13091749 · Cited by 8 · see citing works
UniForensics: Face Forgery Detection via General Facial Representation
Fang, Z., Zhao, H., Wei, T., Zhou, W., Wan, M., Wang, Z., Zhang, W., & Yu, N. (2024) · arXiv · doi:10.48550/arxiv.2407.19079 · Cited by 8 · see citing works
Localization and detection of deepfake videos based on self-blending method
Xu, J., Liu, X., Lin, W., Shang, W., & Wang, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-88523-1 · Cited by 10 · see citing works
Cross-Domain Deepfake Detection Based on Latent Domain Knowledge Distillation
Wang, C., Meng, L., Xia, Z., Ren, N., & Ma, B. (2025) · IEEE Signal Processing Letters · doi:10.1109/lsp.2025.3540941 · Cited by 7 · see citing works
Revisiting face forgery detection towards generalization
Peng, C., Chen, T., Liu, D., Guo, H., Wang, N., & Gao, X. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107310 · Cited by 7 · see citing works
Deepfake Detection Leveraging Self-Blended Artifacts Guided by Facial Embedding Discrepancy
Shan-shan, H., Li, S., Wang, S., Yuan, L., Zhang, Y., & Gao, X. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3585724 · Cited by 7 · see citing works
Forensics-Bench: A Comprehensive Forgery Detection Benchmark Suite for Large Vision Language Models
Wang, J., Lv, C., Li, X., Dong, S., Li, H., Yao, K., Li, C., Shao, W., & Luo, P. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.00400 · Cited by 7 · see citing works
Self-citationUniForensics: Face Forgery Detection via General Facial Representation
Fang, Z., Zhao, H., Wei, T., Zhou, W., Wan, M., Wang, Z., Zhang, W., & Yu, N. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3627420 · Cited by 9 · see citing works
Vulnerability-Aware Spatio-Temporal Learning for Generalizable Deepfake Video Detection
Nguyen, D., Astrid, M., Kacem, A., Ghorbel, E., & Aouada, D. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01004 · Cited by 11 · see citing works
Face forgery detection by progressively enhancing spatial and frequency-aware features
Qi, Y., Wen, S., Zhang, H., Liang, A., Chen, H., & Cao, P. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01357-1 · Cited by 6 · see citing works
AniFaceDiff: Animating stylized avatars via parametric conditioned diffusion models
Chen, K., Seneviratne, S., Wang, W., Hu, D., Saha, S., Hasan, M. T., Rasnayaka, S., Malepathirana, T., Gong, M., & Halgamuge, S. K. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112017 · Cited by 7 · see citing works
Exploring Deepfake Detection: Techniques, Datasets and Challenges
Rana, P., & Bansal, S. R. (2024) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/160156 · Cited by 7 · see citing works
Countering Personalized Text-to-Image Generation with Influence Watermarks
Liu, H., Sun, Z., & Mu, Y. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01165 · Cited by 6 · see citing works
Beyond the Screen: Evaluating Deepfake Detectors under Moiré Pattern Effects
Tariq, R., Heo, M., Woo, S. S., & Tariq, S. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00446 · Cited by 7 · see citing works
Learning spatial‐frequency interaction for generalizable deepfake detection
Zhai, T., Lu, K., Li, J., Wang, Y., Zhang, W., Yu, P., & Xia, Z. (2024) · IET Image Processing · doi:10.1049/ipr2.13276 · Cited by 7 · see citing works
Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges
Liu, P., Tao, Q., & Zhou, J. T. (2024) · arXiv · doi:10.48550/arxiv.2406.06965 · Cited by 6 · see citing works
Adversarial Samples Generated by Self-Forgery for Face Forgery Detection
Duan, H., Jiang, Q., Xu, X., Wang, Y., Yi, H., Yao, S., & Jin, X. (2025) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2025.3529026 · Cited by 6 · see citing works
Texture, Shape and Order Matter: A New Transformer Design for Sequential DeepFake Detection
Li, Y., Li, Y., Wang, X., Wu, B., Zhou, J., & Dong, J. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00030 · Cited by 5 · see citing works
DC-BiNet: Towards interpretable generated image detection with dark channel prior
Tan, D., Niu, C., Yang, Y., Yang, D., & Tan, B. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127508 · Cited by 6 · see citing works
Dual Frequency-Guided Spatiotemporal Feature Learning for Face Forgery Detection
Duan, J., Liu, S., Hao, Y., Huang, H., & He, R. (2025) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2025.3646181 · Cited by 5 · see citing works
NullSwap: Proactive Identity Cloaking Against Deepfake Face Swapping
Wang, T., Niu, S., Cheng, H., Zhang, X., & Wang, Y. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00927 · Cited by 5 · see citing works
2D-Malafide: Adversarial Attacks Against Face Deepfake Detection Systems
Galdi, C., Panariello, M., Todisco, M., & Evans, N. (2024) · International Conference of the Biometrics Special Interest Group (BIOSIG) · doi:10.1109/biosig61931.2024.10786754 · Cited by 6 · see citing works
Generalized Deepfake Detection Using Identity, Behavioral, and Geometric Signatures
Farooq, M. U., Khan, A. A., Haq, I. U., & Malik, K. M. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3588348 · Cited by 7 · see citing works
Learnable Information-Preserving Image Resizer for Face Forgery Detection
She, H., Hu, Y., Liu, B., Li, J., & Li, C. (2023) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3330316 · Cited by 5 · see citing works
High-resolution network-based multi-feature fusion for generalized forgery detection
Liu, R., Zhang, S., Xu, Y., Xu, W., & He, X. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01626-z · Cited by 4 · see citing works
Identity-Agnostic Learning for Deepfake Face Detection
Zhou, X., Deng, Z., & Zhao, Q. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888411 · Cited by 4 · see citing works
- Authors
- Dong, S., Wang, J., Ji, R., Liang, J., Fan, H., & Ge, Z.
- Affiliations
- Megvii (China) (CN)
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 3994-4004
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386071484
- Cited by
- 220
- References
- 75
- Citations by year
- 2026: 57 · 2025: 95 · 2024: 62 · 2023: 6
- FWCI
- 23.53
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- deepfake detection, generalization, identity representation, cross-dataset evaluation, binary classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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