Contrastive Multi-FaceForensics: An End-to-end Bi-grained Contrastive Learning Approach for Multi-face Forgery Detection

DeepFakes have raised serious societal concerns, leading to a great surge in detection-based forensics methods in recent years. Face forgery recognition is the conventional detection method that usually follows a two-phase pipeline: it extracts the face first and then determines its authenticity by classification. Since DeepFakes in the wild usually contain multiple faces, using face forgery detection methods is merely practical as they have to process faces in a sequel, i.e., only one face is processed at the same time. One straightforward way to address this issue is to integrate face extraction and forgery detection in an end-to-end fashion by adapting advanced object detection architectures. However, as these object detection architectures are designed to capture the semantic information of different object categories rather than the subtle forgery traces among the faces, the direct adaptation is far from optimal. In this paper, we describe a new end-to-end framework, Contrastive Multi-FaceForensics (COMICS), to enhance multi-face forgery detection. The core of the proposed framework is a novel bi-grained contrastive learning approach that explores effective face forgery traces at both the coarse- and fine-grained levels. Specifically, the coarse-grained level contrastive learning captures the discriminative features among positive and negative proposal pairs in multiple scales with the instruction of the proposal generator, and the fine-grained level contrastive learning captures the pixel-wise discrepancy between the forged and original areas of the same face and the pixel-wise content inconsistency between different faces. Extensive experiments on the OpenForensics dataset demonstrate our method outperforms other counterparts by a large margin (~18.5%) and shows great potential for integration into various architectures.