A Survey of Threats Against Voice Authentication and Anti-Spoofing Systems

TL;DR

This survey reviews the evolving threat landscape against voice authentication and anti-spoofing systems, covering attack types, methodologies, datasets, and challenges to guide future secure system development.

Contribution

It provides a comprehensive, organized overview of modern threats, attack methodologies, and open challenges in voice authentication and anti-spoofing systems.

Findings

Identifies key attack vectors like deepfake and adversarial attacks.

Summarizes datasets and performance benchmarks.

Highlights open challenges for system robustness.

Abstract

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.