Securing Voice Authentication Applications Against Targeted Data Poisoning

• URL: http://arxiv.org/abs/2406.17277v2
• Date: Tue, 01 Oct 2024 08:16:52 GMT
• Title: Securing Voice Authentication Applications Against Targeted Data Poisoning
• Authors: Alireza Mohammadi, Keshav Sood, Asef Nazari, Dhananjay Thiruvady,
• Abstract summary: 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 of the dataset is poisoned.
• Score: 4.856070170902535
• License:
• 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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