  High-performance anti-spoofing models for automatic speaker verification
(ASV), have been widely used to protect ASV by identifying and filtering
spoofing audio that is deliberately generated by text-to-speech, voice
conversion, audio replay, etc. However, it has been shown that high-performance
anti-spoofing models are vulnerable to adversarial attacks. Adversarial
attacks, that are indistinguishable from original data but result in the
incorrect predictions, are dangerous for anti-spoofing models and not in
dispute we should detect them at any cost. To explore this issue, we proposed
to employ Mockingjay, a self-supervised learning based model, to protect
anti-spoofing models against adversarial attacks in the black-box scenario.
Self-supervised learning models are effective in improving downstream task
performance like phone classification or ASR. However, their effect in defense
for adversarial attacks has not been explored yet. In this work, we explore the
robustness of self-supervised learned high-level representations by using them
in the defense against adversarial attacks. A layerwise noise to signal ratio
(LNSR) is proposed to quantize and measure the effectiveness of deep models in
countering adversarial noise. Experimental results on the ASVspoof 2019 dataset
demonstrate that high-level representations extracted by Mockingjay can prevent
the transferability of adversarial examples, and successfully counter black-box
attacks.
