Vulnerabilities of Audio-Based Biometric Authentication Systems Against Deepfake Speech Synthesis

• URL: http://arxiv.org/abs/2601.02914v1
• Date: Tue, 06 Jan 2026 10:55:32 GMT
• Title: Vulnerabilities of Audio-Based Biometric Authentication Systems Against Deepfake Speech Synthesis
• Authors: Mengze Hong, Di Jiang, Zeying Xie, Weiwei Zhao, Guan Wang, Chen Jason Zhang,
• Abstract summary: Modern voice cloning models trained on very small samples can easily bypass commercial speaker verification systems.<n>Anti-spoofing detectors struggle to generalize across different methods of audio synthesis.
• Score: 21.895422296912454
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As audio deepfakes transition from research artifacts to widely available commercial tools, robust biometric authentication faces pressing security threats in high-stakes industries. This paper presents a systematic empirical evaluation of state-of-the-art speaker authentication systems based on a large-scale speech synthesis dataset, revealing two major security vulnerabilities: 1) modern voice cloning models trained on very small samples can easily bypass commercial speaker verification systems; and 2) anti-spoofing detectors struggle to generalize across different methods of audio synthesis, leading to a significant gap between in-domain performance and real-world robustness. These findings call for a reconsideration of security measures and stress the need for architectural innovations, adaptive defenses, and the transition towards multi-factor authentication.

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