  DeepFakes have raised serious societal concerns, leading to a great surge in
detection-based forensics methods in recent years. Face forgery recognition is
a standard detection method that usually follows a two-phase pipeline. While
those methods perform well in ideal experimental environment, they face
challenges when dealing with DeepFakes in the wild involving complex background
and multiple faces of varying sizes. Moreover, most face forgery recognition
methods can only process one face at a time. One straightforward way to address
this issue is to simultaneous process multi-face by integrating 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 discriminative features of different object categories
rather than the subtle forgery traces among the faces, the direct adaptation
suffers from limited representation ability. In this paper, we propose COMICS,
an end-to-end framework for multi-face forgery detection. COMICS integrates
face extraction and forgery detection in a seamless manner and adapts to
advanced object detection architectures. The proposed bi-grained contrastive
learning approach explores face forgery traces at both the coarse- and
fine-grained levels. Specifically, coarse-grained level contrastive learning
captures the discriminative features among positive and negative proposal pairs
at multiple layers produced by the proposal generator, and 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 among
different faces. Extensive experiments on the OpenForensics and FFIW datasets
demonstrate that our method outperforms other counterparts and shows great
potential for being integrated into various architectures.
