Toward Generalizable Deepfake Detection via Forgery-Aware Audio–Visual Adaptation: A Variational Bayesian Approach

Summary

The paper proposes FoVB (Forgery-aware Audio-Visual Adaptation with Variational Bayes), a framework for multi-modal deepfake detection. It models audio-visual correlation as a Gaussian latent variable estimated via variational Bayes, using difference convolutions and a high-pass filter to extract forgery traces from both modalities, then factorizes the variable with an orthogonality constraint. The authors report that FoVB outperforms other state-of-the-art methods across various benchmarks.

Topics

• Deepfakes and impersonation

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