FauForensics: Boosting Audio-Visual Deepfake Detection With Facial Action Units

The rapid evolution of generative AI has intensified the threat of realistic audio-visual deepfakes, demanding robust and generalizable detection methods. Existing solutions primarily address unimodal (e.g., audio, visual) forgeries but struggle with multimodal manipulations due to inadequate handling of heterogeneous modality features and poor cross-dataset generalization. We propose FauForensics, a novel framework leveraging biologically invariant facial action units (FAUs), which are quantitative descriptors of facial muscle activity linked to emotion physiology. They serve as forgery-resistant representations that reduce domain dependency while capturing subtle synthetic-content disruptions. In addition, unlike prior clip-level comparisons, our method computes frame-wise audio-visual similarities via a fusion module with learnable cross-modal queries, dynamically aligning lip-audio relationships and mitigating feature heterogeneity. Experiments on four publicly available datasets show state-of-the-art performance with 5.17% average cross-dataset improvement over existing methods.