Voice Recognition Systems (VRSs) employ deep learning for speech recognition and speaker recognition. They have been widely deployed in various real-world applications, from intelligent voice assistance to telephony surveillance and biometric authentication. However, prior research has revealed the vulnerability of VRSs to backdoor attacks, which pose a significant threat to the security and privacy of VRSs. Unfortunately, existing literature lacks a thorough review on this topic. This paper fills this research gap by conducting a comprehensive survey on backdoor attacks against VRSs. We first present an overview of VRSs and backdoor attacks, elucidating their basic knowledge. Then we propose a set of evaluation criteria to assess the performance of backdoor attack methods. Next, we present a comprehensive taxonomy of backdoor attacks against VRSs from different perspectives and analyze the characteristic of different categories. After that, we comprehensively review existing attack methods and analyze their pros and cons based on the proposed criteria. Furthermore, we review classic backdoor defense methods and generic audio defense techniques. Then we discuss the feasibility of deploying them on VRSs. Finally, we figure out several open issues and further suggest future research directions to motivate the research of VRSs security.

中文翻译：

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针对语音识别系统的后门攻击：一项调查


语音识别系统 （VRS） 采用深度学习进行语音识别和说话人识别。它们已广泛部署在各种实际应用中，从智能语音协助到电话监控和生物识别身份验证。然而，先前的研究揭示了 VRS 容易受到后门攻击，这对 VRS 的安全性和隐私构成重大威胁。不幸的是，现有文献缺乏对该主题的深入审查。本文通过对针对 VRS 的后门攻击进行全面调查来填补这一研究空白。我们首先概述了 VRS 和后门攻击，阐明了它们的基础知识。然后，我们提出了一套评估标准来评估后门攻击方法的性能。接下来，我们从不同角度对针对 VRS 的后门攻击进行了全面的分类，并分析了不同类别的特征。之后，我们全面回顾了现有的攻击方法，并根据提出的标准分析了它们的优缺点。此外，我们回顾了经典的后门防御方法和通用的音频防御技术。然后，我们讨论了在 VRS 上部署它们的可行性。最后，我们找出了几个悬而未决的问题，并进一步提出了未来的研究方向，以激励 VRSs 安全性的研究。