Self-Supervised Transformer with Domain Adaptive Reconstruction for
  General Face Forgery Video Detection
Daichi Zhang Zihao Xiao Jianmin Li Shiming Ge
Face forgery videos have caused severe social public concern, and various
detectors have been proposed recently. However, most of them are trained in a
supervised manner with limited generalization when detecting videos from
different forgery methods or real source videos. To tackle this issue, we
explore to take full advantage of the difference between real and forgery
videos by only exploring the common representation of real face videos. In this
paper, a Self-supervised Transformer cooperating with Contrastive and
Reconstruction learning (CoReST) is proposed, which is first pre-trained only
on real face videos in a self-supervised manner, and then fine-tuned a linear
head on specific face forgery video datasets. Two specific auxiliary tasks
incorporated contrastive and reconstruction learning are designed to enhance
the representation learning. Furthermore, a Domain Adaptive Reconstruction
(DAR) module is introduced to bridge the gap between different forgery domains
by reconstructing on unlabeled target videos when fine-tuning. Extensive
experiments on public datasets demonstrate that our proposed method performs
even better than the state-of-the-art supervised competitors with impressive
generalization.