Mixture of Low-Rank Adapter Experts in Generalizable Audio Deepfake Detection
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912590544764928 |
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| author | Laakkonen, Janne Kukanov, Ivan Hautamäki, Ville |
| author_facet | Laakkonen, Janne Kukanov, Ivan Hautamäki, Ville |
| contents | Foundation models such as Wav2Vec2 excel at representation learning in speech tasks, including audio deepfake detection. However, after being fine-tuned on a fixed set of bonafide and spoofed audio clips, they often fail to generalize to novel deepfake methods not represented in training. To address this, we propose a mixture-of-LoRA-experts approach that integrates multiple low-rank adapters (LoRA) into the model's attention layers. A routing mechanism selectively activates specialized experts, enhancing adaptability to evolving deepfake attacks. Experimental results show that our method outperforms standard fine-tuning in both in-domain and out-of-domain scenarios, reducing equal error rates relative to baseline models. Notably, our best MoE-LoRA model lowers the average out-of-domain EER from 8.55\% to 6.08\%, demonstrating its effectiveness in achieving generalizable audio deepfake detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13878 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mixture of Low-Rank Adapter Experts in Generalizable Audio Deepfake Detection Laakkonen, Janne Kukanov, Ivan Hautamäki, Ville Audio and Speech Processing Machine Learning Sound Foundation models such as Wav2Vec2 excel at representation learning in speech tasks, including audio deepfake detection. However, after being fine-tuned on a fixed set of bonafide and spoofed audio clips, they often fail to generalize to novel deepfake methods not represented in training. To address this, we propose a mixture-of-LoRA-experts approach that integrates multiple low-rank adapters (LoRA) into the model's attention layers. A routing mechanism selectively activates specialized experts, enhancing adaptability to evolving deepfake attacks. Experimental results show that our method outperforms standard fine-tuning in both in-domain and out-of-domain scenarios, reducing equal error rates relative to baseline models. Notably, our best MoE-LoRA model lowers the average out-of-domain EER from 8.55\% to 6.08\%, demonstrating its effectiveness in achieving generalizable audio deepfake detection. |
| title | Mixture of Low-Rank Adapter Experts in Generalizable Audio Deepfake Detection |
| topic | Audio and Speech Processing Machine Learning Sound |
| url | https://arxiv.org/abs/2509.13878 |