  Deepfake has emerged for several years, yet efficient detection techniques
could generalize over different manipulation methods require further research.
While current image-level detection method fails to generalize to unseen
domains, owing to the domain-shift phenomenon brought by CNN's strong inductive
bias towards Deepfake texture, video-level one shows its potential to have both
generalization across multiple domains and robustness to compression. We argue
that although distinct face manipulation tools have different inherent bias,
they all disrupt the consistency between frames, which is a natural
characteristic shared by authentic videos. Inspired by this, we proposed a
detection approach by capturing frame inconsistency that broadly exists in
different forgery techniques, termed unearthing-common-inconsistency (UCI).
Concretely, the UCI network based on self-supervised contrastive learning can
better distinguish temporal consistency between real and fake videos from
multiple domains. We introduced a temporally-preserved module method to
introduce spatial noise perturbations, directing the model's attention towards
temporal information. Subsequently, leveraging a multi-view cross-correlation
learning module, we extensively learn the disparities in temporal
representations between genuine and fake samples. Extensive experiments
demonstrate the generalization ability of our method on unseen Deepfake
domains.
