Protecting Celebrities From DeepFake With Identity Consistency Transformer
Xiaoyi Dong, Jianmin Bao, Dongdong Chen, Ting Zhang, Weiming Zhang, Nenghai Yu, Dong Chen, Fang Wen
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.
BibTeX
@inproceedings{cvpr2022_protectingcelebr,
title = {Protecting Celebrities From DeepFake With Identity Consistency Transformer},
author = {Xiaoyi Dong and Jianmin Bao and Dongdong Chen and Ting Zhang and Weiming Zhang and Nenghai Yu and Dong Chen and Fang Wen and Baining Guo},
booktitle = {CVPR 2022},
year = {2022}
}