HierCon: Hierarchical Contrastive Attention for Audio Deepfake Detection
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866908802381512704 |
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| author | Liang, Zhili Nicholas Han, Soyeon Caren Wang, Qizhou Leckie, Christopher |
| author_facet | Liang, Zhili Nicholas Han, Soyeon Caren Wang, Qizhou Leckie, Christopher |
| contents | Audio deepfakes generated by modern TTS and voice conversion systems are increasingly difficult to distinguish from real speech, raising serious risks for security and online trust. While state-of-the-art self-supervised models provide rich multi-layer representations, existing detectors treat layers independently and overlook temporal and hierarchical dependencies critical for identifying synthetic artefacts. We propose HierCon, a hierarchical layer attention framework combined with margin-based contrastive learning that models dependencies across temporal frames, neighbouring layers, and layer groups, while encouraging domain-invariant embeddings. Evaluated on ASVspoof 2021 DF and In-the-Wild datasets, our method achieves state-of-the-art performance (1.93% and 6.87% EER), improving over independent layer weighting by 36.6% and 22.5% respectively. The results and attention visualisations confirm that hierarchical modelling enhances generalisation to cross-domain generation techniques and recording conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_01032 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HierCon: Hierarchical Contrastive Attention for Audio Deepfake Detection Liang, Zhili Nicholas Han, Soyeon Caren Wang, Qizhou Leckie, Christopher Sound Artificial Intelligence Audio and Speech Processing Audio deepfakes generated by modern TTS and voice conversion systems are increasingly difficult to distinguish from real speech, raising serious risks for security and online trust. While state-of-the-art self-supervised models provide rich multi-layer representations, existing detectors treat layers independently and overlook temporal and hierarchical dependencies critical for identifying synthetic artefacts. We propose HierCon, a hierarchical layer attention framework combined with margin-based contrastive learning that models dependencies across temporal frames, neighbouring layers, and layer groups, while encouraging domain-invariant embeddings. Evaluated on ASVspoof 2021 DF and In-the-Wild datasets, our method achieves state-of-the-art performance (1.93% and 6.87% EER), improving over independent layer weighting by 36.6% and 22.5% respectively. The results and attention visualisations confirm that hierarchical modelling enhances generalisation to cross-domain generation techniques and recording conditions. |
| title | HierCon: Hierarchical Contrastive Attention for Audio Deepfake Detection |
| topic | Sound Artificial Intelligence Audio and Speech Processing |
| url | https://arxiv.org/abs/2602.01032 |