用元学习+低秩适配器，实现语音深度伪造的零样本泛化检测
Generalizable speech deepfake detection via meta-learned LoRA
- 在自监督模型中插入低秩适配器，仅训练适配器参数
- 仅更新360万参数（全微调1.1%），跨5个数据集表现更优
- 适配器聚焦跨攻击类型的稳定特征，误报率降低至5.30%
可靠的语音深度伪造检测需在攻击分布变化时仍有效。本文将其建模为领域泛化问题，发现将低秩适配器（LoRA）插入自监督（SSL）骨干网络的每个注意力头，并仅用元学习领域泛化（MLDG）训练这些适配器，可获得优异的零样本性能。该模型仅更新约360万参数，约为全微调3.18亿参数的1.1%，却在六个评估语料库中的五个上超越全微调模型。一阶MLDG循环促使适配器关注跨攻击类型持续存在的特征，使平均等错误率（EER）从全微调模型的8.84%降至5.30%。结果表明，结合元学习与参数高效适配，是实现零样本、分布偏移感知语音深度伪造检测的有效方法。
原文摘要 · Abstract (English)
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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