Fetching the paper…
Reading the bibliography…
Face forgery videos have caused severe public concerns, and many detectors have been proposed.
A. Argyriou, T. Evgeniou, and M. Pontil, “Multi-task feature learning,” Adv. Neural Inform. Process. Syst. , pp. 41–48, 2006
2006
Earlier work this paper cites.
V. D. M. Laurens and G. Hinton, “Visualizing data using t-sne,” J. Mach. Learning Research , vol. 9, no. 2605, pp. 2579–2605, 2008
2008
Earlier work this paper cites.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, and Y. Bengio, “Generative adversarial nets,” in Adv. Neural Inform. Process. Syst. , 2014, pp. 2672–2680
2014
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 IEEE Proc. Conf. Comput. Vis. Pattern Recog. , 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 IEEE Proc. Conf. Comput. Vis. Pattern Recog. , 2016, pp. 770–778
2016
Earlier work this paper cites.
M. Ghifary, W. B. Kleijn, M. Zhang, D. Balduzzi, and W. Li, “Deep reconstruction-classification networks for unsupervised domain adaptation,” in Eur. Conf. Comput. Vis. , 2016, pp. 597–613
2016
Earlier work this paper cites.
D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” in IEEE Proc. Conf. Comput. Vis. Pattern Recog. , 2016, pp. 2536–2544
2016
Earlier work this paper cites.
S. Suwajanakorn, S. M. Seitz, and I. Kemelmacher-Shlizerman, “Synthesizing obama: learning lip sync from audio,” ACM Trans. Graph. , vol. 36, no. 4, pp. 95:1–13, 2017
2017
Earlier work this paper cites.
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, “Adversarial discriminative domain adaptation,” in IEEE Proc. Conf. Comput. Vis. Pattern Recog. , 2017, pp. 2962–2971
2017
Earlier work this paper cites.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in IEEE/CVF Int. Conf. Comput. Vis. , 2017, pp. 618–626
2017
Earlier work this paper cites.
D. Afchar, V. Nozick, J. Yamagishi, and I. Echizen, “Mesonet: a compact facial video forgery detection network,” in Int. Workshop on Information Forensics and Security , 2018, pp. 1–7
2018
Earlier work this paper cites.
Z. Murez, S. Kolouri, D. J. Kriegman, R. Ramamoorthi, and K. Kim, “Image to image translation for domain adaptation,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2018, pp. 4500–4509
2018
Earlier work this paper cites.
J. S. Chung, A. Nagrani, and A. Zisserman, “Voxceleb2: Deep speaker recognition,” in Interspeech , 2018, pp. 1086–1090
2018
Earlier work this paper cites.
A. Ephrat, I. Mosseri, O. Lang, T. Dekel, K. Wilson, A. Hassidim, W. T. Freeman, and M. Rubinstein, “Looking to listen at the cocktail party: a speaker-independent audio-visual model for speech separation,” ACM Trans. Graph. , vol. 37, no. 4, p. 112, 2018
2018
Earlier work this paper cites.
D. Güera and E. J. Delp, “Deepfake video detection using recurrent neural networks,” in IEEE international conference on advanced video and signal based surveillance (AVSS) , 2018, pp. 1–6
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. Rössler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, and M. Nießner, “Faceforensics++: Learning to detect manipulated facial images,” in IEEE/CVF Int. Conf. Comput. Vis. , 2019, pp. 1–11
2019
Earlier work this paper cites.
E. Sabir, J. Cheng, A. Jaiswal, W. AbdAlmageed, I. Masi, and P. Natarajan, “Recurrent convolutional strategies for face manipulation detection in videos,” in IEEE Conf. Comput. Vis. Pattern Recog. Worksh. , 2019, pp. 80–87
2019
Earlier work this paper cites.
Y. Li and S. Lyu, “Exposing deepfake videos by detecting face warping artifacts,” in IEEE Conf. Comput. Vis. Pattern Recog. Worksh. , 2019, pp. 46–52
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 Int. Conf. on Biometrics: Theory, Applications and Systems , 2019, pp. 1–8
2019
Earlier work this paper cites.
P. Ramachandran, N. Parmar, A. Vaswani, I. Bello, A. Levskaya, and J. Shlens, “Stand-alone self-attention in vision models,” Adv. Neural Inform. Process. Syst. , pp. 68–80, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
N. Dufour and A. Gully, “Contributing data to deepfake detection research,” in https://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.html , 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
J. Thies, M. Zollhöfer, and M. Nießner, “Deferred neural rendering: image synthesis using neural textures,” ACM Trans. Graph. , vol. 38, no. 4, pp. 66:1–12, 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 IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2020, pp. 5001–5010
2020
Cited alongside, same era.
Y. Tian, D. Krishnan, and P. Isola, “Contrastive multiview coding,” in Eur. Conf. Comput. Vis. , 2020, pp. 776–794
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 Eur. Conf. Comput. Vis. , 2020, pp. 86–103
2020
Cited alongside, same era.
I. Masi, A. Killekar, R. M. Mascarenhas, S. P. Gurudatt, and W. AbdAlmageed, “Two-branch recurrent network for isolating deepfakes in videos,” in Eur. Conf. Comput. Vis. , 2020, pp. 667–684
2020
Cited alongside, same era.
X. Li, Y. Lang, Y. Chen, X. Mao, Y. He, S. Wang, H. Xue, and Q. Lu, “Sharp multiple instance learning for deepfake video detection,” in ACM Int. Conf. Multimedia , 2020, pp. 1864–1872
D. Zhang, F. Lin, Y. Hua, P. Wang, D. Zeng, and S. Ge, “Deepfake video detection with spatiotemporal dropout transformer,” in ACM Int. Conf. Multimedia , 2022, pp. 5833–5841
2022
Later among the works it cites.
2022
Later among the works it cites.
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. B. Girshick, “Masked autoencoders are scalable vision learners,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2022, pp. 15 979–15 988
2022
Later among the works it cites.
Z. Gu, Y. Chen, T. Yao, S. Ding, J. Li, and L. Ma, “Delving into the local: Dynamic inconsistency learning for deepfake video detection,” in AAAI Conf. on Artificial Intelligence , 2022, pp. 744–752
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
L. Chai, D. Bau, S.-N. Lim, and P. Isola, “What makes fake images detectable? understanding properties that generalize,” in Eur. Conf. Comput. Vis. , 2020, pp. 103–120
2020
Cited alongside, same era.
Y. Li, X. Yang, P. Sun, H. Qi, and S. Lyu, “Celeb-DF: A large-scale challenging dataset for deepfake forensics,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2020, pp. 3204–3213
2020
Cited alongside, same era.
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 IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2020, pp. 2886–2895
2020
Cited alongside, same era.
S.-Y. Wang, O. Wang, R. Zhang, A. Owens, and A. A. Efros, “CNN-generated images are surprisingly easy to spot… for now,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2020, pp. 8695–8704
2020
Cited alongside, same era.
D. Zhang, C. Li, F. Lin, D. Zeng, and S. Ge, “Detecting Deepfake Videos with Temporal Dropout 3DCNN,” in Int. Joint Conf. Artif. Intell. , 2021, pp. 1288–1294
2021
Cited alongside, same era.
Z. Hu, H. Xie, Y. Wang, J. Li, Z. Wang, and Y. Zhang, “Dynamic inconsistency-aware deepfake video detection,” in Int. Joint Conf. Artif. Intell. , 2021, pp. 736–742
2021
Cited alongside, same era.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” in Int. Conf. on Learning Representations , 2021
2021
Cited alongside, same era.
J. Guan, H. Zhou, Z. Hong, E. Ding, J. Wang, C. Quan, and Y. Zhao, “Delving into sequential patches for deepfake detection,” in Adv. Neural Inform. Process. Syst. , 2022, pp. 4517–4530
2022
Later among the works it cites.
R. Wang, Z. Wu, Z. Weng, J. Chen, G.-J. Qi, and Y.-G. Jiang, “Cross-domain contrastive learning for unsupervised domain adaptation,” IEEE Trans. Multimedia , vol. 25, pp. 1665–1673, 2022
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 IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2022, pp. 18 689–18 698
2022
Later among the works it cites.
L. Zhang, T. Qiao, M. Xu, N. Zheng, and S. Xie, “Unsupervised learning-based framework for deepfake video detection,” IEEE Trans. Multimedia , vol. 25, pp. 4785–4799, 2022
2022
Later among the works it cites.
A. Haliassos, R. Mira, S. Petridis, and M. Pantic, “Leveraging real talking faces via self-supervision for robust forgery detection,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2022, pp. 14 930–14 942
2022
Later among the works it cites.
L. Li, T. Zhou, W. Wang, L. Yang, J. Li, and Y. Yang, “Locality-aware inter-and intra-video reconstruction for self-supervised correspondence learning,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2022, pp. 8719–8730
2022
Later among the works it cites.
Q. Gu, S. Chen, T. Yao, Y. Chen, S. Ding, and R. Yi, “Exploiting fine-grained face forgery clues via progressive enhancement learning,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 1, 2022, pp. 735–743
2022
Later among the works it cites.
Y. Ni, D. Meng, C. Yu, C. Quan, D. Ren, and Y. Zhao, “Core: Consistent representation learning for face forgery detection,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2022, pp. 12–21
2022
Later among the works it cites.
R. Shao, T. Wu, and Z. Liu, “Detecting and recovering sequential deepfake manipulation,” in Eur. Conf. Comput. Vis. , 2022, pp. 712–728
2022
Later among the works it cites.
Y. Yu, R. Ni, Y. Zhao, S. Yang, F. Xia, N. Jiang, and G. Zhao, “Msvt: Multiple spatiotemporal views transformer for deepfake video detection,” IEEE Trans. Circuit Syst. Video Technol. , vol. 33, no. 9, pp. 4462–4471, 2023
2023
Closest in time.
Z. Guo, G. Yang, J. Chen, and X. Sun, “Exposing deepfake face forgeries with guided residuals,” IEEE Trans. Multimedia , vol. 25, pp. 8458–8470, 2023
2023
Closest in time.
Y. Yu, X. Zhao, R. Ni, S. Yang, Y. Zhao, and A. C. Kot, “Augmented multi-scale spatiotemporal inconsistency magnifier for generalized deepfake detection,” IEEE Transactions on Multimedia , vol. 25, pp. 8487–8498, 2023
2023
Closest in time.
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 IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2023, pp. 3994–4004
2023
Closest in time.
C. Zhao, C. Wang, G. Hu, H. Chen, C. Liu, and J. Tang, “Istvt: interpretable spatial-temporal video transformer for deepfake detection,” EEE Trans. on Inform. Forensics and Security , vol. 18, pp. 1335–1348, 2023
2023
Closest in time.
T. Wang and K. P. Chow, “Noise based deepfake detection via multi-head relative-interaction,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 12, 2023, pp. 14 548–14 556
2023
Closest in time.
Z. Wang, J. Bao, W. Zhou, W. Wang, and H. Li, “Altfreezing for more general video face forgery detection,” in IEEE/CVF Conf. Comput. Vis. Pattern Recog. , 2023, pp. 4129–4138
2023
Closest in time.
D. Liu, Z. Zheng, C. Peng, Y. Wang, N. Wang, and X. Gao, “Hierarchical forgery classifier on multi-modality face forgery clues,” IEEE Trans. Multimedia , vol. 26, pp. 2894–2905, 2024
2024
Closest in time.
Z. Yin, J. Wang, Y. Xiao, H. Zhao, T. Li, W. Zhou, A. Liu, and X. Liu, “Improving deepfake detection generalization by invariant risk minimization,” IEEE Trans. Multimedia , vol. 26, pp. 6785–6798, 2024
2024
Closest in time.
S. Xiao, G. Lan, J. Yang, W. Lu, Q. Meng, and X. Gao, “MCS-GAN: A different understanding for generalization of deep forgery detection,” IEEE Trans. Multimedia , vol. 26, pp. 1333–1345, 2024
2024
Closest in time.
T. Qiao, S. Xie, Y. Chen, F. Retraint, R. Shi, and X. Luo, “Deepfake detection fighting against noisy label attack,” IEEE Trans. Multimedia , pp. 1–14, 2024
2024
Closest in time.