用扩散模型生成难例，提升语音深度伪造检测的泛化能力
Diffusion Reconstruction towards Generalizable Audio Deepfake Detection
- 通过扩散模型生成难以分辨的伪造音频样本
- 在未见攻击下平均错误率降低，优于基线模型
- 适合需要强泛化能力的语音安全检测场景
在语音深度伪造检测（ADD）中，应对未知攻击的鲁棒泛化仍面临挑战，主要源于生成模型的快速演进。为此，我们提出以难例分类为核心的框架：能够区分困难样本的模型自然也能有效处理简单样本。我们研究了多种重建范式，发现基于扩散的方法最适于生成难例。此外，通过多层特征聚合和引入正则化辅助对比学习（RACL）目标，进一步提升了模型泛化能力。实验表明，所提方法在未见攻击下表现出显著更强的泛化性能，最佳模型相较基线实现平均等错误率（EER）的大幅下降。
原文摘要 · Abstract (English)
Achieving robust generalization against unseen attacks remains a challenge in Audio Deepfake Detection (ADD), driven by the rapid evolution of generative models. To address this, we propose a framework centered on hard sample classification. The core idea is that a model capable of distinguishing challenging hard samples is inherently equipped to handle simpler cases effectively. We investigate multiple reconstruction paradigms, identifying the diffusion-based method as optimal for generating hard samples. Furthermore, we leverage multi-layer feature aggregation and introduce a Regularization-Assisted Contrastive Learning (RACL) objective to enhance generalizability. Experiments demonstrate the superior generalization of our approach, with our best model achieving a significant reduction in the average Equal Error Rate (EER) compared to the baseline.
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