While voiceprint authentication offers convenient user authentication and access control through voice feature recognition, a critical research gap remains: existing voiceprint authentication systems fail to simultaneously achieve sound security against replay, spoofing, and adversarial attacks, preserve voice privacy leakage, and satisfy usability demand. Previous efforts have struggled to balance these issues comprehensively. To bridge this gap, we present SeVoAuth, a cloud-based Voiceprint Authentication as a Service (VAaaS) system designed to provide privacy preservation, robust security, and enhanced usability. SeVoAuth stores a synthesized voiceprint of a user in the cloud during user registration, thereby safeguarding the privacy of the real voiceprint of the user. During user authentication, SeVoAuth applies a hash function to continuously transform features of the synthesized voiceprint, dynamically generating new verification targets for voiceprint feature mapping in each authentication session. This dynamic transformation approach effectively mitigates replay, spoofing, and adversarial attacks without requiring complex user interactions. We conduct a thorough analysis on the security and privacy of SeVoAuth and proceed to implement a prototype for performance evaluation through a series of user tests. Experimental results demonstrate that SeVoAuth outperforms cutting-edge approaches, achieving an average authentication accuracy of 99.47%, and an average Precise Detection Rate (PDR) of 98.35% against various attacks. SeVoAuth is evaluated as highly secure, efficient, and user-friendly across various circumstances.

Voiceprint Authentication as a Service (VAaS) offers great convenience due to ubiquity, generality, and usability. Despite its attractiveness, it suffers from user voiceprint leakage over the air or at the cloud, which intrudes user voice privacy and retards its wide adoption. The literature still lacks an effective solution on this issue. Traditional methods based on cryptography are too complex to be practically deployed while other approaches distort user voiceprints, which hinders accurate user identification. In this article, we propose a leakage resilient user authentication cloud service with privacy preservation based on random personal voice challenges, named VOLERE (VOice LEakage REsilient). It applies a novel voiceprint synthesis method based on a Log Magnitude Approximate (LMA) vocal tract model to fuse original voices of different speaking modes in order to generate a synthesized voiceprint for authentication. Thus, raw voiceprints of users can be well protected. We implement VOLERE and conduct a series of user tests. Experimental results show sound performance of VOLERE regarding authentication accuracy, efficiency, stability, leakage resilience and user acceptance. In particular, its authentication accuracy is reasonably stable regardless user nationality, gender, age, elapsed time, and environment, as well as variance of speaking modes.

Voiceprint authentication provides great convenience to users in many application scenarios. However, it easily suffers from spoofing attacks including speech synthesis, speech conversion, and speech replay. Liveness detection is an effective way to resist these attacks. But existing methods suffer from many disadvantages, such as extra deployment costs due to precise data collection, environmental disturbance, high computational overhead, and operational complexity. A uniform platform that can offer voiceprint authentication as a service (VAaS) over the cloud is also lacked. Hence, it is imperative to design an economic and effective method for liveness detection in voiceprint authentication. In this article, we propose a novel liveness detection method named LiVoAuth for voiceprint authentication. It applies a randomly generated vector sequence as liveness detection mode (LDM), corresponding to a random challenge code used for authentication. We implement LiVoAuth and conduct a series of user studies to evaluate its performance in terms of accuracy, stability, efficiency, security, and user acceptance. Experimental results demonstrate its advantages compared with cutting-edge methods

Voiceprint authentication is a promising approach for user authentication due to its low acquisition cost and non-contact characteristics. However, most of existing voiceprint authentication systems directly store user voiceprint templates in a database, without taking any effective measures to protect user privacy. Once the voiceprint templates in the database are leaked, user voice information becomes public, which brings severe security threats to the users and impact practical applications of a voiceprint authentication system. Few literature works study privacy-preserving voiceprint authentication although it is particularly needed. Most of existing solutions based on cryptography suffer from high computational costs. But usability of some efficient systems is poor due to additional interactions with users. To tackle the above issues, this paper proposes VoiceSketch, a privacy-preserving voiceprint authentication system based on a secure sketch to protect the privacy of user voiceprint templates with high usability. VoiceSketch does not store the voiceprint templates directly. Instead, it uses a secure sketch to correct errors between multiple voiceprint features of the same user and use cryptographic hash function to extract an authentication key from a user’s voiceprint template. We analyze the security of VoiceSketch based on secure sketch and hash function. We also implement VoiceSketch and design a series of experiments to verify its accuracy and effectiveness.

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.