  Despite encouraging progress in deepfake detection, generalization to unseen
forgery types remains a significant challenge due to the limited forgery clues
explored during training. In contrast, we notice a common phenomenon in
deepfake: fake video creation inevitably disrupts the statistical regularity in
original videos. Inspired by this observation, we propose to boost the
generalization of deepfake detection by distinguishing the "regularity
disruption" that does not appear in real videos. Specifically, by carefully
examining the spatial and temporal properties, we propose to disrupt a real
video through a Pseudo-fake Generator and create a wide range of pseudo-fake
videos for training. Such practice allows us to achieve deepfake detection
without using fake videos and improves the generalization ability in a simple
and efficient manner. To jointly capture the spatial and temporal disruptions,
we propose a Spatio-Temporal Enhancement block to learn the regularity
disruption across space and time on our self-created videos. Through
comprehensive experiments, our method exhibits excellent performance on several
datasets.
