  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 .
