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Deepfakes have recently raised significant trust issues and security concerns among the public.
J. Ma, Z. Zhao, X. Yi, J. Chen, L. Hong, and E. H. Chi, “Modeling task relationships in multi-task learning with multi-gate mixture-of-experts,” in Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining , 2018, pp. 1930–1939
1939
Earlier work this paper cites.
R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton, “Adaptive mixtures of local experts,” Neural computation , vol. 3, no. 1, pp. 79–87, 1991
1991
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009, pp. 248–255
2009
Earlier work this paper cites.
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, “Joint face detection and alignment using multitask cascaded convolutional networks,” IEEE signal processing letters , vol. 23, no. 10, pp. 1499–1503, 2016
2016
Earlier work this paper cites.
J. Thies, M. Zollhofer, M. Stamminger, C. Theobalt, and M. Nießner, “Face2face: Real-time face capture and reenactment of rgb videos,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2387–2395
2016
Earlier work this paper cites.
F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 1251–1258
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Y. Li, M.-C. Chang, and S. Lyu, “In ictu oculi: Exposing ai created fake videos by detecting eye blinking,” in 2018 IEEE International Workshop on Information Forensics and Security (WIFS) . IEEE, 2018, pp. 1–7
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
D. Li, Y. Yang, Y.-Z. Song, and T. Hospedales, “Learning to generalize: Meta-learning for domain generalization,” in Proceedings of the AAAI conference on artificial intelligence , vol. 32, no. 1, 2018
2018
Earlier work this paper cites.
X. Yang, Y. Li, and S. Lyu, “Exposing deep fakes using inconsistent head poses,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 8261–8265
2019
Earlier work this paper cites.
M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in International Conference on Machine Learning . PMLR, 2019, pp. 6105–6114
2019
Earlier work this paper cites.
H. H. Nguyen, J. Yamagishi, and I. Echizen, “Capsule-forensics: Using capsule networks to detect forged images and videos,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 2307–2311
2019
Earlier work this paper cites.
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-efficient transfer learning for nlp,” in International Conference on Machine Learning . PMLR, 2019, pp. 2790–2799
2019
Earlier work this paper cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al. , “Pytorch: An imperative style, high-performance deep learning library,” Advances in neural information processing systems , vol. 32, 2019
2019
Earlier work this paper cites.
A. Rossler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, and M. Nießner, “Faceforensics++: Learning to detect manipulated facial images,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 1–11
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
https://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.html
2019
Earlier work this paper cites.
J. Thies, M. Zollhöfer, and M. Nießner, “Deferred neural rendering: Image synthesis using neural textures,” Acm Transactions on Graphics (TOG) , vol. 38, no. 4, pp. 1–12, 2019
2019
Earlier work this paper cites.
L. Li, J. Bao, T. Zhang, H. Yang, D. Chen, F. Wen, and B. Guo, “Face x-ray for more general face forgery detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 5001–5010
2020
Earlier work this paper cites.
I. Masi, A. Killekar, R. M. Mascarenhas, S. P. Gurudatt, and W. AbdAlmageed, “Two-branch recurrent network for isolating deepfakes in videos,” in European Conference on Computer Vision . Springer, 2020, pp. 667–684
2020
Earlier work this paper cites.
B. Shi, D. Zhang, Q. Dai, Z. Zhu, Y. Mu, and J. Wang, “Informative dropout for robust representation learning: A shape-bias perspective,” in International Conference on Machine Learning . PMLR, 2020, pp. 8828–8839
2020
Earlier work this paper cites.
K. Xu, M. Qin, F. Sun, Y. Wang, Y.-K. Chen, and F. Ren, “Learning in the frequency domain,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 1740–1749
2020
Earlier work this paper cites.
Y. Qian, G. Yin, L. Sheng, Z. Chen, and J. Shao, “Thinking in frequency: Face forgery detection by mining frequency-aware clues,” in European Conference on Computer Vision . Springer, 2020, pp. 86–103
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Y. Li, X. Yang, P. Sun, H. Qi, and S. Lyu, “Celeb-df: A large-scale challenging dataset for deepfake forensics,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 3207–3216
2020
Earlier work this paper cites.
U. A. Ciftci, I. Demir, and L. Yin, “Fakecatcher: Detection of synthetic portrait videos using biological signals,” IEEE transactions on pattern analysis and machine intelligence , 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Z. Yu, C. Zhao, Z. Wang, Y. Qin, Z. Su, X. Li, F. Zhou, and G. Zhao, “Searching central difference convolutional networks for face anti-spoofing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 5295–5305
2020
Earlier work this paper cites.
B. Zi, M. Chang, J. Chen, X. Ma, and Y.-G. Jiang, “Wilddeepfake: A challenging real-world dataset for deepfake detection,” in Proceedings of the 28th ACM International Conference on Multimedia , 2020, pp. 2382–2390
2020
Earlier work this paper cites.
L. Jiang, R. Li, W. Wu, C. Qian, and C. C. Loy, “Deeperforensics-1.0: A large-scale dataset for real-world face forgery detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 2889–2898
2020
Cited alongside, same era.
C. Kong, B. Chen, W. Yang, H. Li, P. Chen, and S. Wang, “Appearance matters, so does audio: Revealing the hidden face via cross-modality transfer,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 32, no. 1, pp. 423–436, 2021
2021
Cited alongside, same era.
Y. Luo, Y. Zhang, J. Yan, and W. Liu, “Generalizing face forgery detection with high-frequency features,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 16 317–16 326
2021
Cited alongside, same era.
J. Li, H. Xie, J. Li, Z. Wang, and Y. Zhang, “Frequency-aware discriminative feature learning supervised by single-center loss for face forgery detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 6458–6467
Y. Zhang, K. Zhou, and Z. Liu, “Neural prompt search,” arXiv preprint arXiv:2206.04673 , 2022
2022
Later among the works it cites.
D. Lian, D. Zhou, J. Feng, and X. Wang, “Scaling & shifting your features: A new baseline for efficient model tuning,” Advances in Neural Information Processing Systems , vol. 35, pp. 109–123, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
B. Mustafa, C. Riquelme, J. Puigcerver, R. Jenatton, and N. Houlsby, “Multimodal contrastive learning with limoe: the language-image mixture of experts,” Advances in Neural Information Processing Systems , vol. 35, pp. 9564–9576, 2022
2022
Later among the works it cites.
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2021
Cited alongside, same era.
2021
Cited alongside, same era.
C. Riquelme, J. Puigcerver, B. Mustafa, M. Neumann, R. Jenatton, A. Susano Pinto, D. Keysers, and N. Houlsby, “Scaling vision with sparse mixture of experts,” Advances in Neural Information Processing Systems , vol. 34, pp. 8583–8595, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
H. Hazimeh, Z. Zhao, A. Chowdhery, M. Sathiamoorthy, Y. Chen, R. Mazumder, L. Hong, and E. Chi, “Dselect-k: Differentiable selection in the mixture of experts with applications to multi-task learning,” Advances in Neural Information Processing Systems , vol. 34, pp. 29 335–29 347, 2021
2021
Cited alongside, same era.
M. Lewis, S. Bhosale, T. Dettmers, N. Goyal, and L. Zettlemoyer, “Base layers: Simplifying training of large, sparse models,” in International Conference on Machine Learning . PMLR, 2021, pp. 6265–6274
2021
Cited alongside, same era.
S. Roller, S. Sukhbaatar, J. Weston et al. , “Hash layers for large sparse models,” Advances in Neural Information Processing Systems , vol. 34, pp. 17 555–17 566, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2022
Later among the works it cites.
H. Bao, W. Wang, L. Dong, Q. Liu, O. K. Mohammed, K. Aggarwal, S. Som, S. Piao, and F. Wei, “Vlmo: Unified vision-language pre-training with mixture-of-modality-experts,” Advances in Neural Information Processing Systems , vol. 35, pp. 32 897–32 912, 2022
2022
Later among the works it cites.
N. Du, Y. Huang, A. M. Dai, S. Tong, D. Lepikhin, Y. Xu, M. Krikun, Y. Zhou, A. W. Yu, O. Firat et al. , “Glam: Efficient scaling of language models with mixture-of-experts,” in International Conference on Machine Learning . PMLR, 2022, pp. 5547–5569
2022
Later among the works it cites.
Y. Zhou, T. Lei, H. Liu, N. Du, Y. Huang, V. Zhao, A. M. Dai, Q. V. Le, J. Laudon et al. , “Mixture-of-experts with expert choice routing,” Advances in Neural Information Processing Systems , vol. 35, pp. 7103–7114, 2022
2022
Later among the works it cites.
W. Fedus, B. Zoph, and N. Shazeer, “Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,” Journal of Machine Learning Research , vol. 23, no. 120, pp. 1–39, 2022
2022
Later among the works it cites.
J. Wang, Y. Sun, and J. Tang, “Lisiam: Localization invariance siamese network for deepfake detection,” IEEE Transactions on Information Forensics and Security , vol. 17, pp. 2425–2436, 2022
2022
Later among the works it cites.
J. Fei, Y. Dai, P. Yu, T. Shen, Z. Xia, and J. Weng, “Learning second order local anomaly for general face forgery detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 20 270–20 280
2022
Later among the works it cites.
H. Wu, J. Zhou, J. Tian, J. Liu, and Y. Qiao, “Robust image forgery detection against transmission over online social networks,” IEEE Transactions on Information Forensics and Security , vol. 17, pp. 443–456, 2022
2022
Later among the works it cites.
S. Dong, J. Wang, R. Ji, J. Liang, H. Fan, and Z. Ge, “Implicit identity leakage: The stumbling block to improving deepfake detection generalization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 3994–4004
2023
Later among the works it cites.
B. Huang, Z. Wang, J. Yang, J. Ai, Q. Zou, Q. Wang, and D. Ye, “Implicit identity driven deepfake face swapping detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 4490–4499
2023
Later among the works it cites.
2023
Later among the works it cites.
C. Miao, Z. Tan, Q. Chu, H. Liu, H. Hu, and N. Yu, “F2trans: High-frequency fine-grained transformer for face forgery detection,” IEEE Transactions on Information Forensics and Security , vol. 18, pp. 1039–1051, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Wang, K. Yu, C. Chen, X. Hu, and S. Peng, “Dynamic graph learning with content-guided spatial-frequency relation reasoning for deepfake detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 7278–7287
2023
Later among the works it cites.
Z. Yan, Y. Zhang, Y. Fan, and B. Wu, “Ucf: Uncovering common features for generalizable deepfake detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 22 412–22 423
2023
Later among the works it cites.
A. Luo, C. Kong, J. Huang, Y. Hu, X. Kang, and A. C. Kot, “Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection,” IEEE Transactions on Information Forensics and Security , vol. 19, pp. 1168–1182, 2024
2024
Closest in time.
J. Deng, C. Lin, P. Hu, C. Shen, Q. Wang, Q. Li, and Q. Li, “Towards benchmarking and evaluating deepfake detection,” IEEE Transactions on Dependable and Secure Computing , 2024
2024
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Z. Yu, R. Cai, Z. Li, W. Yang, J. Shi, and A. C. Kot, “Benchmarking joint face spoofing and forgery detection with visual and physiological cues,” IEEE Transactions on Dependable and Secure Computing , vol. 21, no. 5, pp. 4327–4342, 2024
2024
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B. Zhang, Q. Yin, W. Lu, and X. Luo, “Deepfake detection and localization using multi-view inconsistency measurement,” IEEE Transactions on Dependable and Secure Computing , 2024
2024
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J. Hu, J. Liang, Z. Qin, X. Liao, W. Zhou, and X. Lin, “Ada-finfer: Inferring face representations from adaptive select frames for high-visual-quality deepfake detection,” IEEE Transactions on Dependable and Secure Computing , 2024
2024
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C. Yu, X. Zhang, Y. Duan, S. Yan, Z. Wang, Y. Xiang, S. Ji, and W. Chen, “Diff-id: An explainable identity difference quantification framework for deepfake detection,” IEEE Transactions on Dependable and Secure Computing , 2024
2024
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2024
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S. Jia, R. Lyu, K. Zhao, Y. Chen, Z. Yan, Y. Ju, C. Hu, X. Li, B. Wu, and S. Lyu, “Can chatgpt detect deepfakes? a study of using multimodal large language models for media forensics,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 4324–4333
2024
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2024
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Z. Yan, Y. Luo, S. Lyu, Q. Liu, and B. Wu, “Transcending forgery specificity with latent space augmentation for generalizable deepfake detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 8984–8994
2024
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2024
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2025
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Y. Zhang, T. Wang, Z. Yu, Z. Gao, L. Shen, and S. Chen, “Mfclip: Multi-modal fine-grained clip for generalizable diffusion face forgery detection,” IEEE Transactions on Information Forensics and Security , 2025
2025
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X. Guo, X. Song, Y. Zhang, X. Liu, and X. Liu, “Rethinking vision-language model in face forensics: Multi-modal interpretable forged face detector,” in Proceedings of the Computer Vision and Pattern Recognition Conference , 2025, pp. 105–116
2025
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