Towards Generating Adversarial Examples on Combined Systems of Automatic Speaker Verification and Spoofing Countermeasure

Abstract

The security of unprotected automatic speaker verification (ASV) system is vulnerable to a variety of spoofing attacks where an attacker (adversary) disguises him/herself as a specific targeted user. It is a common practice to use spoofing countermeasure (CM) to improve the security of ASV systems so as to avoid illegal access. However, recent studies have shown that both ASV and CM systems are vulnerable to adversarial attacks. Previous researches mainly focus on adversarial attacks on a single ASV or CM system. But in practical scenarios, ASVs are typically deployed in conjunction with CM. In this paper, we investigate attacking the tandem system of ASV and CM with adversarial examples. The joint objective function is designed to restrict the generating process of adversarial examples. The joint gradient of the ASV and CM system is derived to generate adversarial examples. Fast Gradient Sign Method (FSGM) and Projected Gradient Descent (PGD) are utilized to study the vulnerability of tandem verification systems against white-box adversarial attacks. Through our attack, audio samples whose original labels are spoof or nontarget can be successfully accepted by the tandem system. Experimental results on the ASVSpoof2019 dataset show that the tandem system is vulnerable to our proposed attack.