aiqbench analysis
HyperPotter addresses the growing threat of high-fidelity audio deepfakes by leveraging hypergraph neural networks to capture complex, synergistic patterns among acoustic features that traditional pairwise models miss. The framework demonstrates significant improvements in error rate reduction across diverse test sets while highlighting remaining vulnerabilities under severe signal distortion. This research advances machine learning techniques for robust adversarial detection within the critical domain of speech and audio processing.
HyperPotter: Spell the Charm of High-Order Interactions in Audio Deepfake Detection
arXiv:2602.05670v2 Announce Type: replace-cross Abstract: 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 designed to capture high-order relations associated with synergistic patterns through clustering-based hyperedges with class-aware prototype initialization. Extensive experiments on 13 test sets show that HyperPotter improves over the baseline on 11 sets, yielding an average relative EER reduction of 12.68\% across all test sets and 22.15\% on the improved sets. These results demonstrate strong cross-scenario generalization, while also revealing robustness limits under severe codec or channel distortion.
Abstract: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 designed to capture high-order relations associated with synergistic patterns through clustering-based hyperedges with class-aware prototype initialization. Extensive experiments on 13 test sets show that HyperPotter improves over the baseline on 11 sets, yielding an average relative EER reduction of 12.68\% across all test sets and 22.15\% on the improved sets. These results demonstrate strong cross-scenario generalization, while also revealing robustness limits under severe codec or channel distortion.
Sound (cs.SD); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)
arXiv:2602.05670 [cs.SD]
(or arXiv:2602.05670v2 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2602.05670 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 228 words