Paper ID: 2405.15173

A3:Ambiguous Aberrations Captured via Astray-Learning for Facial Forgery Semantic Sublimation

Xinan He, Yue Zhou, Wei Ye, Feng Ding

Prior DeepFake detection methods have faced a core challenge in preserving generalizability and fairness effectively. In this paper, we proposed an approach akin to decoupling and sublimating forgery semantics, named astray-learning. The primary objective of the proposed method is to blend hybrid forgery semantics derived from high-frequency components into authentic imagery, named aberrations. The ambiguity of aberrations is beneficial to reducing the model's bias towards specific semantics. Consequently, it can enhance the model's generalization ability and maintain the detection fairness. All codes for astray-learning are publicly available at https://anonymous.4open.science/r/astray-learning-C49B .

Submitted: May 24, 2024