The method decomposes facial landmark motions into bases with an autoencoder, selectively breaks their natural correlations to create kinematic inconsistencies, and uses these to train a detector that achieves state-of-the-art generalization on deepfake video benchmarks.
Deepshield: Forti- fying deepfake video detection with local and global forgery analysis
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Beyond Flicker: Detecting Kinematic Inconsistencies for Generalizable Deepfake Video Detection
The method decomposes facial landmark motions into bases with an autoencoder, selectively breaks their natural correlations to create kinematic inconsistencies, and uses these to train a detector that achieves state-of-the-art generalization on deepfake video benchmarks.