  Generalizability to unseen forgery types is crucial for face forgery
detectors. Recent works have made significant progress in terms of
generalization by synthetic forgery data augmentation. In this work, we explore
another path for improving the generalization. Our goal is to reduce the
features that are easy to learn in the training phase, so as to reduce the risk
of overfitting on specific forgery types. Specifically, in our method, a
teacher network takes as input the face images and generates an attention map
of the deep features by a diverse multihead attention ViT. The attention map is
used to guide a student network to focus on the low-attended features by
reducing the highly-attended deep features. A deep feature mixup strategy is
also proposed to synthesize forgeries in the feature domain. Experiments
demonstrate that, without data augmentation, our method is able to achieve
promising performances on unseen forgeries and highly compressed data.
