  One of the most pressing challenges for the detection of face-manipulated
videos is generalising to forgery methods not seen during training while
remaining effective under common corruptions such as compression. In this
paper, we examine whether we can tackle this issue by harnessing videos of real
talking faces, which contain rich information on natural facial appearance and
behaviour and are readily available in large quantities online. Our method,
termed RealForensics, consists of two stages. First, we exploit the natural
correspondence between the visual and auditory modalities in real videos to
learn, in a self-supervised cross-modal manner, temporally dense video
representations that capture factors such as facial movements, expression, and
identity. Second, we use these learned representations as targets to be
predicted by our forgery detector along with the usual binary forgery
classification task; this encourages it to base its real/fake decision on said
factors. We show that our method achieves state-of-the-art performance on
cross-manipulation generalisation and robustness experiments, and examine the
factors that contribute to its performance. Our results suggest that leveraging
natural and unlabelled videos is a promising direction for the development of
more robust face forgery detectors.
