Vulnerabilities of Audio-Based Biometric Authentication Systems Against Deepfake Speech Synthesis
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866911356558508032 |
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| author | Hong, Mengze Jiang, Di Xie, Zeying Zhao, Weiwei Wang, Guan Zhang, Chen Jason |
| author_facet | Hong, Mengze Jiang, Di Xie, Zeying Zhao, Weiwei Wang, Guan Zhang, Chen Jason |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_02914 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Vulnerabilities of Audio-Based Biometric Authentication Systems Against Deepfake Speech Synthesis Hong, Mengze Jiang, Di Xie, Zeying Zhao, Weiwei Wang, Guan Zhang, Chen Jason Sound Cryptography and Security 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. |
| title | Vulnerabilities of Audio-Based Biometric Authentication Systems Against Deepfake Speech Synthesis |
| topic | Sound Cryptography and Security |
| url | https://arxiv.org/abs/2601.02914 |