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://github.com/leaffeall/DFS-GDD.

翻译：本文提出了一种新颖的深度伪造检测方法，通过语义解耦来提升检测的泛化能力。当前存在多种深度伪造技术，它们不仅具有独特的伪造语义，也可能共享某些共同的伪造语义。独特的伪造语义以及不相关的内容语义可能导致深度伪造检测器过拟合，从而损害其泛化能力。在我们提出的方法中，经过解耦后，可以从深度伪造内容中提取出共同的伪造语义，并随后用于提升深度伪造检测器的泛化能力。此外，为了追求更强的泛化性，我们设计了一个自适应高通滤波模块和一个两阶段训练策略，以提升解耦语义的独立性。在FF++、Celeb-DF、DFD和DFDC数据集上的评估表明，我们的方法具有优异的检测和泛化性能。代码发布于：https://github.com/leaffeall/DFS-GDD。