Protecting Celebrities From DeepFake With Identity Consistency Transformer

Xiaoyi Dong, Jianmin Bao, Dongdong Chen, Ting Zhang, Weiming Zhang, Nenghai Yu, Dong Chen, Fang Wen, Baining Guo; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 9468-9478

Abstract

In this work we propose Identity Consistency Transformer, a novel face forgery detection method that focuses on high-level semantics, specifically identity information, and detecting a suspect face by finding identity inconsistency in inner and outer face regions. The Identity Consistency Transformer incorporates a consistency loss for identity consistency determination. We show that Identity Consistency Transformer exhibits superior generalization ability not only across different datasets but also across various types of image degradation forms found in real-world applications including deepfake videos. The Identity Consistency Transformer can be easily enhanced with additional identity information when such information is available, and for this reason it is especially well-suited for detecting face forgeries involving celebrities.

Related Material

[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Dong_2022_CVPR, author = {Dong, Xiaoyi and Bao, Jianmin and Chen, Dongdong and Zhang, Ting and Zhang, Weiming and Yu, Nenghai and Chen, Dong and Wen, Fang and Guo, Baining}, title = {Protecting Celebrities From DeepFake With Identity Consistency Transformer}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {9468-9478} }