Voting for the right answer: Adversarial defense for speaker verification
Journal
Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Journal Volume
6
Pages
4655-4659
Date Issued
2021
Author(s)
Abstract
Automatic speaker verification (ASV) is a well developed technology for biometric identification, and has been ubiquitous implemented in security-critic applications, such as banking and access control. However, previous works have shown that ASV is under the radar of adversarial attacks, which are very similar to their original counterparts from human's perception, yet will manipulate the ASV render wrong prediction. Due to the very late emergence of adversarial attacks for ASV, effective countermeasures against them are limited. Given that the security of ASV is of high priority, in this work, we propose the idea of "voting for the right answer"to prevent risky decisions of ASV in blind spot areas, by employing random sampling and voting. Experimental results show that our proposed method improves the robustness against both the limited-knowledge attackers by pulling the adversarial samples out of the blind spots, and the sufficient-knowledge attackers by introducing randomness and increasing the attackers' budgets. Copyright ? 2021 ISCA.
Subjects
Adversarial attack
Speaker verification
Access control
Budget control
Speech communication
Automatic speaker verification
Biometric identifications
Blind spots
Human perception
Random sampling
Risky decisions
Speech recognition
Type
conference paper