A Survey of Threats Against Voice Authentication and Anti-Spoofing Systems
It addresses security vulnerabilities in voice authentication systems used across finance, smart devices, and law enforcement, but is incremental as it synthesizes existing literature without proposing new solutions.
This survey comprehensively reviews modern threats to voice authentication and anti-spoofing systems, including data poisoning, adversarial, and deepfake attacks, by summarizing methodologies, datasets, and performance to highlight emerging risks and open challenges.
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.