MSCT: Differential Cross-Modal Attention for Deepfake Detection

• URL: http://arxiv.org/abs/2604.07741v1
• Date: Thu, 09 Apr 2026 02:56:16 GMT
• Title: MSCT: Differential Cross-Modal Attention for Deepfake Detection
• Abstract summary: deepfake detection typically employs a complementary multi-modal model to check the forgery traces in the video.<n>Traditional multi-modal forgery detection method has the problem of insufficient feature extraction and modal alignment deviation.<n>Our approach includes a multi-scale self-attention to integrate the features of adjacent embeddings and a differential cross-modal attention to fuse multi-modal features.
• Score: 8.183317636944706
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Audio-visual deepfake detection typically employs a complementary multi-modal model to check the forgery traces in the video. These methods primarily extract forgery traces through audio-visual alignment, which results from the inconsistency between audio and video modalities. However, the traditional multi-modal forgery detection method has the problem of insufficient feature extraction and modal alignment deviation. To address this, we propose a multi-scale cross-modal transformer encoder (MSCT) for deepfake detection. Our approach includes a multi-scale self-attention to integrate the features of adjacent embeddings and a differential cross-modal attention to fuse multi-modal features. Our experiments demonstrate competitive performance on the FakeAVCeleb dataset, validating the effectiveness of the proposed structure.

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