  Automatic Speaker Verification (ASV), increasingly used in security-critical
applications, faces vulnerabilities from rising adversarial attacks, with few
effective defenses available. In this paper, we propose a neural codec-based
adversarial sample detection method for ASV. The approach leverages the codec's
ability to discard redundant perturbations and retain essential information.
Specifically, we distinguish between genuine and adversarial samples by
comparing ASV score differences between original and re-synthesized audio (by
codec models). This comprehensive study explores all open-source neural codecs
and their variant models for experiments. The Descript-audio-codec model stands
out by delivering the highest detection rate among 15 neural codecs and
surpassing seven prior state-of-the-art (SOTA) detection methods. Note that,
our single-model method even outperforms a SOTA ensemble method by a large
margin.
