  Audio-visual deepfake detection scrutinizes manipulations in public video
using complementary multimodal cues. Current methods, which train on fused
multimodal data for multimodal targets face challenges due to uncertainties and
inconsistencies in learned representations caused by independent modality
manipulations in deepfake videos. To address this, we propose cross-modality
and within-modality regularization to preserve modality distinctions during
multimodal representation learning. Our approach includes an audio-visual
transformer module for modality correspondence and a cross-modality
regularization module to align paired audio-visual signals, preserving modality
distinctions. Simultaneously, a within-modality regularization module refines
unimodal representations with modality-specific targets to retain
modal-specific details. Experimental results on the public audio-visual
dataset, FakeAVCeleb, demonstrate the effectiveness and competitiveness of our
approach.
