Towards Generalizable Deepfake Detection via Forgery-aware Audio-Visual Adaptation: A Variational Bayesian Approach

• URL: http://arxiv.org/abs/2511.19080v1
• Date: Mon, 24 Nov 2025 13:20:03 GMT
• Title: Towards Generalizable Deepfake Detection via Forgery-aware Audio-Visual Adaptation: A Variational Bayesian Approach
• Authors: Fan Nie, Jiangqun Ni, Jian Zhang, Bin Zhang, Weizhe Zhang, Bin Li,
• Abstract summary: Forgery-aware Audio-Visual Adaptation with Variational Bayes (FoVB) is developed.<n>We exploit various difference convolutions and a high-pass filter to discern local and global forgery traces from both modalities.<n>Our FoVB outperforms other state-of-the-art methods in various benchmarks.
• Score: 31.10567291555587
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The widespread application of AIGC contents has brought not only unprecedented opportunities, but also potential security concerns, e.g., audio-visual deepfakes. Therefore, it is of great importance to develop an effective and generalizable method for multi-modal deepfake detection. Typically, the audio-visual correlation learning could expose subtle cross-modal inconsistencies, e.g., audio-visual misalignment, which serve as crucial clues in deepfake detection. In this paper, we reformulate the correlation learning with variational Bayesian estimation, where audio-visual correlation is approximated as a Gaussian distributed latent variable, and thus develop a novel framework for deepfake detection, i.e., Forgery-aware Audio-Visual Adaptation with Variational Bayes (FoVB). Specifically, given the prior knowledge of pre-trained backbones, we adopt two core designs to estimate audio-visual correlations effectively. First, we exploit various difference convolutions and a high-pass filter to discern local and global forgery traces from both modalities. Second, with the extracted forgery-aware features, we estimate the latent Gaussian variable of audio-visual correlation via variational Bayes. Then, we factorize the variable into modality-specific and correlation-specific ones with orthogonality constraint, allowing them to better learn intra-modal and cross-modal forgery traces with less entanglement. Extensive experiments demonstrate that our FoVB outperforms other state-of-the-art methods in various benchmarks.

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