Shared-Private Cross-Modal Discrepancy Learning for Imbalanced Multi-Source Audio-Visual Deepfake Detection

Audio-visual deepfake detectors are often trained under a single dataset or manipulation regime, but multi-source training collections may contain heterogeneous attacks and uneven source distributions. Training separate detectors for each source is inefficient, while a single shared detector may be biased toward dominant source-specific patterns. We study known-source imbalanced multi-source audio-visual deepfake detection using FakeAVCeleb and LAV-DF, where source identity is supplied during training and inference. To address this setting, we build on the Multi-Scale Cross-Modal Transformer Encoder (MSCT) and propose Pre-MSCT Shared-Private Feature Fusion. Our method routes \texttt{[CLS]...

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