Exposing AI-generated videos with motion magnification

Recent progress of artificial intelligence makes it easier to edit facial movements in videos or create face substitutions, bringing new challenges to anti-fake-faces techniques. Although multimedia forensics provides many detection algorithms from a traditional point of view, it is increasingly hard to discriminate the fake videos from real ones while they become more sophisticated and plausible with updated forgery technologies. In this paper, we introduce a motion discrepancy based method that can effectively differentiate AI-generated fake videos from real ones. The amplitude of face motions in videos is first magnified, and fake videos will show more serious distortion or flicker than the pristine videos. We pre-trained a deep CNN on frames extracted from the training videos and the output vectors of the frame sequences are used as input of an LSTM at secondary training stage. Our approach is evaluated over a large fake video dataset Faceforensics++ produced by various advanced generation technologies, it shows superior performance contrasted to existing pixel-based fake video forensics approaches.