Generalizable speech deepfake detection via meta-learned LoRA

Janne Laakkonen; Ivan Kukanov; Ville Hautamäki

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arXiv · 2502.10838

Generalizable speech deepfake detection via meta-learned LoRA

Janne Laakkonen·Ivan Kukanov·Ville Hautamäki

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Abstract

Reliable detection of speech deepfakes (spoofs) must remain effective when the distribution of spoofing attacks shifts. We frame the task as domain generalization and show that inserting Low-Rank Adaptation (LoRA) adapters into every attention head of a self-supervised (SSL) backbone, then training only those adapters with Meta-Learning Domain Generalization (MLDG), yields strong zero-shot performance. The resulting model updates about 3.6 million parameters, roughly 1.1% of the 318 million updated in full fine-tuning, yet surpasses a fully fine-tuned counterpart on five of six evaluation corpora. A first-order MLDG loop encourages the adapters to focus on cues that persist across attack types, lowering the average EER from 8.84% for the fully fine-tuned model to 5.30% with our best MLDG-LoRA configuration. Our findings show that combining meta-learning with parameter-efficient adaptation offers an effective method for zero-shot, distribution-shift-aware speech deepfake detection.

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BibTeX RIS

Janne Laakkonen, Ivan Kukanov, Ville Hautamäki. 2025-02-15. Generalizable speech deepfake detection via meta-learned LoRA. https://arxiv.org/abs/2502.10838

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