用元学习提升语音伪造检测对未知攻击的泛化能力。
Meta-Learning Approaches for Improving Detection of Unseen Speech Deepfakes
- 基于元学习学习抗攻击特征，仅需少量样本即可适应新攻击。
- 在InTheWild数据集上EER从21.67%降至10.42%，仅用96个样本。
- 适合需要快速更新、应对新型语音伪造的实时安全系统。
当前语音伪造检测方法在已知攻击下表现良好，但对未见过的攻击泛化能力仍不足。社交平台上语音伪造的泛滥凸显了系统应对未知攻击的需求。本文从元学习角度出发，旨在学习攻击无关特征，以极少量样本适应未知攻击。该方法具有优势，因大规模训练数据生成往往成本高昂或不可行。实验表明，在InTheWild数据集上，仅使用96个未见攻击样本，等错误率（EER）从21.67%降至10.42%。持续的少样本自适应确保系统保持最新状态。
原文摘要 · Abstract (English)
Current speech deepfake detection approaches perform satisfactorily against known adversaries; however, generalization to unseen attacks remains an open challenge. The proliferation of speech deepfakes on social media underscores the need for systems that can generalize to unseen attacks not observed during training. We address this problem from the perspective of meta-learning, aiming to learn attack-invariant features to adapt to unseen attacks with very few samples available. This approach is promising since generating of a high-scale training dataset is often expensive or infeasible. Our experiments demonstrated an improvement in the Equal Error Rate (EER) from 21.67% to 10.42% on the InTheWild dataset, using just 96 samples from the unseen dataset. Continuous few-shot adaptation ensures that the system remains up-to-date.
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