  In this paper, we propose a novel method for detecting DeepFakes, enhancing
the generalization of detection through semantic decoupling. There are now
multiple DeepFake forgery technologies that not only possess unique forgery
semantics but may also share common forgery semantics. The unique forgery
semantics and irrelevant content semantics may promote over-fitting and hamper
generalization for DeepFake detectors. For our proposed method, after
decoupling, the common forgery semantics could be extracted from DeepFakes, and
subsequently be employed for developing the generalizability of DeepFake
detectors. Also, to pursue additional generalizability, we designed an adaptive
high-pass module and a two-stage training strategy to improve the independence
of decoupled semantics. Evaluation on FF++, Celeb-DF, DFD, and DFDC datasets
showcases our method's excellent detection and generalization performance. Code
is available at: https://anonymous.4open.science/r/DFS-GDD-0F42.
