  Recent deep learning based video synthesis approaches, in particular with
applications that can forge identities such as "DeepFake", have raised great
security concerns. Therefore, corresponding deep forensic methods are proposed
to tackle this problem. However, existing methods are either based on
unexplainable deep networks which greatly degrades the principal
interpretability factor to media forensic, or rely on fragile image statistics
such as noise pattern, which in real-world scenarios can be easily deteriorated
by data compression. In this paper, we propose an fully-interpretable video
forensic method that is designed specifically to expose deep-faked videos. To
enhance generalizability on videos with various content, we model the temporal
motion of multiple specific spatial locations in the videos to extract a robust
and reliable representation, called Co-Motion Pattern. Such kind of conjoint
pattern is mined across local motion features which is independent of the video
contents so that the instance-wise variation can also be largely alleviated.
More importantly, our proposed co-motion pattern possesses both superior
interpretability and sufficient robustness against data compression for
deep-faked videos. We conduct extensive experiments to empirically demonstrate
the superiority and effectiveness of our approach under both classification and
anomaly detection evaluation settings against the state-of-the-art deep
forensic methods.
