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Existing face forgery detection usually follows the paradigm of training models in a single domain, which leads to limited generalization capacity when unseen scenarios and unknown attacks occur.
S. Masoudnia and R. Ebrahimpour, “Mixture of experts: a literature survey,” Artificial Intelligence Review , vol. 42, pp. 275–293, 2014
2014
Earlier work this paper cites.
R. Girshick, “Fast r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1440–1448
2015
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.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
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.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in International conference on machine learning , 2017, pp. 1126–1135
2017
Earlier work this paper cites.
D. Güera and E. J. Delp, “Deepfake video detection using recurrent neural networks,” in 2018 15th IEEE international conference on advanced video and signal based surveillance (AVSS) . IEEE, 2018, pp. 1–6
2018
Earlier work this paper cites.
D. Afchar, V. Nozick, J. Yamagishi, and I. Echizen, “Mesonet: a compact facial video forgery detection network,” in 2018 IEEE international workshop on information forensics and security (WIFS) . IEEE, 2018, pp. 1–7
2018
Earlier work this paper cites.
R. Rothe, R. Timofte, and L. V. Gool, “Deep expectation of real and apparent age from a single image without facial landmarks,” International Journal of Computer Vision , vol. 126, no. 2-4, pp. 144–157, 2018
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.
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.
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.
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.
——, “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.
X. Wang, Z. Cai, D. Gao, and N. Vasconcelos, “Towards universal object detection by domain attention,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 7289–7298
2019
Earlier work this paper cites.
H. H. Nguyen, F. Fang, J. Yamagishi, and I. Echizen, “Multi-task learning for detecting and segmenting manipulated facial images and videos,” in 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS) . IEEE, 2019, pp. 1–8
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, F. Fang, J. Yamagishi, and I. Echizen, “Multi-task learning for detecting and segmenting manipulated facial images and videos,” in 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS) . IEEE, 2019, pp. 1–8
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.
J. Frank, T. Eisenhofer, L. Schönherr, A. Fischer, D. Kolossa, and T. Holz, “Leveraging frequency analysis for deep fake image recognition,” in International conference on machine learning . PMLR, 2020, pp. 3247–3258
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.
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.
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.
2020
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
Cited alongside, same era.
2020
Cited alongside, same era.
H. Huang, L. Lin, R. Tong, H. Hu, Q. Zhang, Y. Iwamoto, X. Han, Y.-W. Chen, and J. Wu, “Unet 3+: A full-scale connected unet for medical image segmentation,” in ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP) . IEEE, 2020, pp. 1055–1059
2020
Cited alongside, same era.
J. Lambert, Z. Liu, O. Sener, J. Hays, and V. Koltun, “Mseg: A composite dataset for multi-domain semantic segmentation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 2879–2888
J. Cao, C. Ma, T. Yao, S. Chen, S. Ding, and X. Yang, “End-to-end reconstruction-classification learning for face forgery detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 4113–4122
2022
Later among the works it cites.
L. Chen, Y. Zhang, Y. Song, L. Liu, and J. Wang, “Self-supervised learning of adversarial example: Towards good generalizations for deepfake detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 18 710–18 719
2022
Later among the works it cites.
R. Zhang, W. Zhang, R. Fang, P. Gao, K. Li, J. Dai, Y. Qiao, and H. Li, “Tip-adapter: Training-free adaption of clip for few-shot classification,” in European Conference on Computer Vision . Springer, 2022, pp. 493–510
2022
Later among the works it cites.
K. Zhou, J. Yang, C. C. Loy, and Z. Liu, “Conditional prompt learning for vision-language models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 16 816–16 825
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2020
Cited alongside, same era.
X. Zhao, S. Schulter, G. Sharma, Y.-H. Tsai, M. Chandraker, and Y. Wu, “Object detection with a unified label space from multiple datasets,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIV 16 . Springer, 2020, pp. 178–193
2020
Cited alongside, same era.
Y. Tian, C. Sun, B. Poole, D. Krishnan, C. Schmid, and P. Isola, “What makes for good views for contrastive learning?” Advances in Neural Information Processing Systems , vol. 33, pp. 6827–6839, 2020
2020
Cited alongside, same era.
J. Deng, J. Guo, E. Ververas, I. Kotsia, and S. Zafeiriou, “Retinaface: Single-shot multi-level face localisation in the wild,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 5203–5212
2020
Cited alongside, same era.
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
Cited alongside, same era.
2020
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.
Z. Chen, L. Xie, S. Pang, Y. He, and B. Zhang, “Magdr: Mask-guided detection and reconstruction for defending deepfakes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 9014–9023
2021
Cited alongside, same era.
A. A. Pokroy and A. D. Egorov, “Efficientnets for deepfake detection: Comparison of pretrained models,” in 2021 IEEE conference of russian young researchers in electrical and electronic engineering (ElConRus) . IEEE, 2021, pp. 598–600
2021
Cited alongside, same era.
2022
Later among the works it cites.
X. Zhai, X. Wang, B. Mustafa, A. Steiner, D. Keysers, A. Kolesnikov, and L. Beyer, “Lit: Zero-shot transfer with locked-image text tuning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 18 123–18 133
2022
Later among the works it cites.
J.-B. Alayrac, J. Donahue, P. Luc, A. Miech, I. Barr, Y. Hasson, K. Lenc, A. Mensch, K. Millican, M. Reynolds et al. , “Flamingo: a visual language model for few-shot learning,” Advances in Neural Information Processing Systems , vol. 35, pp. 23 716–23 736, 2022
2022
Later among the works it cites.
H. Cao, Y. Wang, J. Chen, D. Jiang, X. Zhang, Q. Tian, and M. Wang, “Swin-unet: Unet-like pure transformer for medical image segmentation,” in European conference on computer vision . Springer, 2022, pp. 205–218
2022
Later among the works it cites.
X. Zhou, V. Koltun, and P. Krähenbühl, “Simple multi-dataset detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 7571–7580
2022
Later among the works it cites.
J. Wang, Z. Wu, W. Ouyang, X. Han, J. Chen, Y.-G. Jiang, and S.-N. Li, “M2tr: Multi-modal multi-scale transformers for deepfake detection,” in Proceedings of the 2022 international conference on multimedia retrieval , 2022, pp. 615–623
2022
Later among the works it cites.
M. Jia, L. Tang, B.-C. Chen, C. Cardie, S. Belongie, B. Hariharan, and S.-N. Lim, “Visual prompt tuning,” in European Conference on Computer Vision . Springer, 2022, pp. 709–727
2022
Later among the works it cites.
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 16 000–16 009
2022
Later among the works it cites.
K. Sun, T. Yao, S. Chen, S. Ding, J. Li, and R. Ji, “Dual contrastive learning for general face forgery detection,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 2, 2022, pp. 2316–2324
2022
Later among the works it cites.
J. Wang, Z. Wu, W. Ouyang, X. Han, J. Chen, Y.-G. Jiang, and S.-N. Li, “M2tr: Multi-modal multi-scale transformers for deepfake detection,” in Proceedings of the 2022 International Conference on Multimedia Retrieval , 2022, pp. 615–623
2022
Later among the works it cites.
J. Li, D. Li, C. Xiong, and S. Hoi, “Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,” in ICML , 2022
2022
Later among the works it cites.
S. Woo et al. , “Add: Frequency attention and multi-view based knowledge distillation to detect low-quality compressed deepfake images,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 1, 2022, pp. 122–130
2022
Later among the works it cites.
H. Song, S. Huang, Y. Dong, and W.-W. Tu, “Robustness and generalizability of deepfake detection: A study with diffusion models,” 2023
2023
Later among the works it cites.
Y. Lai, Z. Luo, and Z. Yu, “Detect any deepfakes: Segment anything meets face forgery detection and localization,” in Chinese Conference on Biometric Recognition , 2023, pp. 180–190
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Yang, J. Liang, Y. Xu, X.-Y. Zhang, and R. He, “Masked relation learning for deepfake detection,” IEEE Transactions on Information Forensics and Security , 2023
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.
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. 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 , 2024
2024
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2024
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C. Kong, K. Zheng, Y. Liu, S. Wang, A. Rocha, and H. Li, “ m 3 m^{3} fas: An accurate and robust multimodal mobile face anti-spoofing system,” IEEE Transactions on Dependable and Secure Computing , 2024
2024
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H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” Advances in neural information processing systems , vol. 36, 2024
2024
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