HyperPotter: Spell the Charm of High-Order Interactions in Audio Deepfake Detection
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866911424369917952 |
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| author | Wen, Qing Li, Haohao Ba, Zhongjie Cheng, Peng He, Miao Lu, Li Ren, Kui |
| author_facet | Wen, Qing Li, Haohao Ba, Zhongjie Cheng, Peng He, Miao Lu, Li Ren, Kui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_05670 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HyperPotter: Spell the Charm of High-Order Interactions in Audio Deepfake Detection Wen, Qing Li, Haohao Ba, Zhongjie Cheng, Peng He, Miao Lu, Li Ren, Kui Sound Artificial Intelligence Audio and Speech Processing 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. |
| title | HyperPotter: Spell the Charm of High-Order Interactions in Audio Deepfake Detection |
| topic | Sound Artificial Intelligence Audio and Speech Processing |
| url | https://arxiv.org/abs/2602.05670 |