The existing deepfake detection methods have reached a bottleneck in generalizing to unseen forgeries and manipulation approaches. Based on the observation that the deepfake detectors exhibit a preference for overfitting the specific primary regions in input, this paper enhances the generalization capability from a novel regularization perspective. This can be simply achieved by augmenting the images through primary region removal, thereby preventing the detector from over-relying on data bias. Our method consists of two stages, namely the static localization for primary region maps, as well as the dynamic exploitation of primary region masks. The proposed method can be seamlessly integrated into different backbones without affecting their inference efficiency. We conduct extensive experiments over three widely used deepfake datasets - DFDC, DF-1.0, and Celeb-DF with five backbones. Our method demonstrates an average performance improvement of 6% across different backbones and performs competitively with several state-of-the-art baselines.

翻译：现有深度伪造检测方法在泛化至未见伪造样本及操作手段方面已遭遇瓶颈。基于深度伪造检测器倾向于过拟合输入中特定主区域的观察，本文从正则化新视角提升其泛化能力。该方法通过移除主区域增强图像，从而防止检测器过度依赖数据偏差。我们提出的方法包含两个阶段：主区域图的静态定位与主区域掩码的动态利用。该方法可无缝集成至不同骨干网络，且不影响其推理效率。我们基于五个骨干网络，在三个广泛使用的深度伪造数据集（DFDC、DF-1.0和Celeb-DF）上进行了大量实验。实验表明，该方法在不同骨干网络上平均性能提升6%，并在多个最先进基线中展现出竞争力。