Recently, automatic speaker verification (ASV) based on deep learning is easily contaminated by adversarial attacks, which is a new type of attack that injects imperceptible perturbations to audio signals so as to make ASV produce wrong decisions. This poses a significant threat to the security and reliability of ASV systems. To address this issue, we propose a Diffusion-Based Adversarial Purification (DAP) method that enhances the robustness of ASV systems against such adversarial attacks. Our method leverages a conditional denoising diffusion probabilistic model to effectively purify the adversarial examples and mitigate the impact of perturbations. DAP first introduces controlled noise into adversarial examples, and then performs a reverse denoising process to reconstruct clean audio. Experimental results demonstrate the efficacy of the proposed DAP in enhancing the security of ASV and meanwhile minimizing the distortion of the purified audio signals.

翻译：最近，基于深度学习的自动说话人验证（ASV）极易受到对抗攻击的污染，这是一种新型攻击方式，通过向音频信号注入难以察觉的扰动，使ASV系统做出错误判断，对ASV系统的安全性与可靠性构成重大威胁。为解决该问题，我们提出了一种基于扩散的对抗净化（DAP）方法，以增强ASV系统对抗此类攻击的鲁棒性。该方法利用条件去噪扩散概率模型，有效净化对抗样本并减轻扰动影响。DAP首先向对抗样本引入受控噪声，随后执行反向去噪过程以重建干净音频。实验结果表明，所提出的DAP方法在提升ASV安全性的同时，能够最小化净化音频信号的失真。