Learning to mask: Towards generalized face forgery detection
This addresses the challenge of robust face forgery detection for security applications, but it is incremental as it builds on existing attention-based methods.
The paper tackles the problem of generalizing face forgery detection to unseen forgery types by reducing overfitting on specific features, achieving promising performance on unseen forgeries and compressed data without data augmentation.
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.