HyperPotter: Spell the Charm of High-Order Interactions in Audio Deepfake Detection

Advances in AIGC technologies have enabled the synthesis of highly realistic audio deepfakes capable of deceiving human auditory perception. Although numerous audio deepfake detection (ADD) methods have been developed, most rely on local temporal/spectral features or pairwise relations, overlooking high-order interactions (HOIs). HOIs capture discriminative patterns that emerge from multiple feature components beyond their individual contributions. We propose HyperPotter, a hypergraph-based framework that explicitly models these synergistic HOIs through clustering-based hyperedges with class-aware prototype initialization. Extensive experiments demonstrate that HyperPotter surpasses its baseline by an average relative gain of 22.15% across 11 datasets and outperforms state-of-the-art methods by 13.96% on 4 challenging cross-domain datasets, demonstrating superior generalization to diverse attacks and speakers.

Key Contributions

• HyperPotter: a hypergraph-based framework that explicitly models high-order interactions (HOIs) among audio features for deepfake detection, going beyond pairwise relations
• Clustering-based hyperedge construction with class-aware prototype initialization to capture synergistic multi-feature patterns
• Achieves 22.15% average relative improvement over baseline across 11 datasets and 13.96% gain over SOTA on 4 cross-domain datasets

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