  Automatic speaker verification (ASV), one of the most important technology
for biometric identification, has been widely adopted in security-critical
applications. However, ASV is seriously vulnerable to recently emerged
adversarial attacks, yet effective countermeasures against them are limited. In
this paper, we adopt neural vocoders to spot adversarial samples for ASV. We
use the neural vocoder to re-synthesize audio and find that the difference
between the ASV scores for the original and re-synthesized audio is a good
indicator for discrimination between genuine and adversarial samples. This
effort is, to the best of our knowledge, among the first to pursue such a
technical direction for detecting time-domain adversarial samples for ASV, and
hence there is a lack of established baselines for comparison. Consequently, we
implement the Griffin-Lim algorithm as the detection baseline. The proposed
approach achieves effective detection performance that outperforms the
baselines in all the settings. We also show that the neural vocoder adopted in
the detection framework is dataset-independent. Our codes will be made
open-source for future works to do fair comparison.
