梳理语音认证的各类攻击手段，助力构建更安全的防伪系统
A Survey of Threats Against Voice Authentication and Anti-Spoofing Systems
- 系统性梳理数据污染、对抗攻击、深度伪造等威胁类型
- 覆盖主流攻击方法与测试数据集，对比性能与局限性
- 适合安全研究者和语音系统开发者参考
语音认证已从依赖人工声学特征的传统系统，演变为能提取鲁棒说话人嵌入的深度学习模型，应用范围扩展至金融、智能设备、执法等领域。然而随着广泛应用，攻击手段也不断升级。本文全面综述针对语音认证系统（VAS）及防伪对策（CMs）的现代威胁，包括数据投毒、对抗攻击、深度伪造和对抗性欺骗攻击。按时间线梳理语音认证的发展，分析漏洞如何随技术进步演变。针对每类攻击，总结方法、常用数据集、性能对比与局限性，并基于公认分类体系组织现有文献。通过揭示新兴风险与开放挑战，旨在推动更安全、更鲁棒的语音认证系统发展。
原文摘要 · Abstract (English)
Voice authentication has undergone significant changes from traditional systems that relied on handcrafted acoustic features to deep learning models that can extract robust speaker embeddings. This advancement has expanded its applications across finance, smart devices, law enforcement, and beyond. However, as adoption has grown, so have the threats. This survey presents a comprehensive review of the modern threat landscape targeting Voice Authentication Systems (VAS) and Anti-Spoofing Countermeasures (CMs), including data poisoning, adversarial, deepfake, and adversarial spoofing attacks. We chronologically trace the development of voice authentication and examine how vulnerabilities have evolved in tandem with technological advancements. For each category of attack, we summarize methodologies, highlight commonly used datasets, compare performance and limitations, and organize existing literature using widely accepted taxonomies. By highlighting emerging risks and open challenges, this survey aims to support the development of more secure and resilient voice authentication systems.
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