DeepReversion: Reversely Inferring the Original Face from the DeepFake Face

Jiaxin Ai, Zhongyuan Wang, Baojin Huang, Zhen Han

Abstract: Deepfake techniques can generate realistic fake images and videos. Malicious fake facial images quickly spread through the Internet, posing a potential threat to personal privacy and judicial forensics. However, the defense methods against deepfake proposed so far mainly focus on the discrimination of authenticity, but cannot identify the true source of the forged face, i.e., the original genuine face corresponding to the face-swapped fake face. This paper poses an interesting issue for face deepfake, which is the proactive forensics of “knowing what and knowing how”. In view of the fact that the fake face exhibits high similarity with the original face, especially the facial expression and pose, we argue that the original face can be approximately estimated from the deepfake counterpart. Accordingly, we advocate a deep-learning-based face inversion approach, so-called DeepReversion, which learns the inverse mapping from the deepfake face to the original face. Based on UNet, we design a specific end-to-end DeepReversion network, and conduct comprehensive experiments on public deepfake datasets. The experimental results show that the speculated face is highly consistent with the original face in terms of visual effects, PSNR, SSIM and similarity given by face recognizers.