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.
Contributing data to deepfake de- tection research.https://research.google/ blog/contributing-data-to-deepfake- detection-research/
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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.