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arXiv · 2509.13878

Mixture of Low-Rank Adapter Experts in Generalizable Audio Deepfake Detection

Janne Laakkonen·Ivan Kukanov·Ville Hautamäki
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Abstract

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.

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Janne Laakkonen, Ivan Kukanov, Ville Hautamäki. 2025-09-17. Mixture of Low-Rank Adapter Experts in Generalizable Audio Deepfake Detection. https://arxiv.org/abs/2509.13878

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