  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.
