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arXiv · 2406.17277

Securing Voice Authentication Applications Against Targeted Data Poisoning

Alireza Mohammadi·Keshav Sood·Asef Nazari·Dhananjay Thiruvady
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Abstract

Deep neural network-based voice authentication systems are promising biometric verification techniques that uniquely identify biological characteristics to verify a user. However, they are particularly susceptible to targeted data poisoning attacks, where attackers replace legitimate users' utterances with their own. We propose an enhanced framework using realworld datasets considering realistic attack scenarios. The results show that the proposed approach is robust, providing accurate authentications even when only a small fraction (5% of the dataset) is poisoned.

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Alireza Mohammadi, Keshav Sood, Asef Nazari, Dhananjay Thiruvady. 2024-06-25. Securing Voice Authentication Applications Against Targeted Data Poisoning. https://arxiv.org/abs/2406.17277

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