  Speaker recognition has become very popular in many application scenarios,
such as smart homes and smart assistants, due to ease of use for remote control
and economic-friendly features. The rapid development of SRSs is inseparable
from the advancement of machine learning, especially neural networks. However,
previous work has shown that machine learning models are vulnerable to
adversarial attacks in the image domain, which inspired researchers to explore
adversarial attacks and defenses in Speaker Recognition Systems (SRS).
Unfortunately, existing literature lacks a thorough review of this topic. In
this paper, we fill this gap by performing a comprehensive survey on
adversarial attacks and defenses in SRSs. We first introduce the basics of SRSs
and concepts related to adversarial attacks. Then, we propose two sets of
criteria to evaluate the performance of attack methods and defense methods in
SRSs, respectively. After that, we provide taxonomies of existing attack
methods and defense methods, and further review them by employing our proposed
criteria. Finally, based on our review, we find some open issues and further
specify a number of future directions to motivate the research of SRSs
security.
