# Adversarial attacks and defenses in Speaker Recognition Systems: A survey

arXiv: https://arxiv.org/abs/2205.13685 · HTML: https://mlsanity.com/paper/2205.13685/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2022.

## Built on by (most cited first, 6 of 6)

- [LMD: A Learnable Mask Network to Detect Adversarial Examples for Speaker Verification](https://mlsanity.com/paper/2211.00825/) (2022)
- [A Survey of Threats Against Voice Authentication and Anti-Spoofing Systems](https://mlsanity.com/paper/2508.16843/) (2025)
- [Unraveling Adversarial Examples against Speaker Identification -- Techniques for Attack Detection and Victim Model Classification](https://mlsanity.com/paper/2402.19355/) (2024)
- [Investigating Vulnerabilities and Defenses Against Audio-Visual Attacks: A Comprehensive Survey Emphasizing Multimodal Models](https://mlsanity.com/paper/2506.11521/) (2025)
- [Spectral Masking and Interpolation Attack (SMIA): A Black-box Adversarial Attack against Voice Authentication and Anti-Spoofing Systems](https://mlsanity.com/paper/2509.07677/) (2025)
- [Addressing Key Challenges of Adversarial Attacks and Defenses in the Tabular Domain: A Methodological Framework for Coherence and Consistency](https://mlsanity.com/paper/2412.07326/) (2024)

## Abstract (arXiv)

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.