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.

翻译：基于深度神经网络的语音认证系统是一种前景广阔的生物特征验证技术，其通过独特识别生物特征来验证用户身份。然而，此类系统尤其容易受到定向数据投毒攻击，即攻击者用自身语音替换合法用户的语音片段。我们提出了一种增强型框架，该框架采用真实世界数据集并考虑了现实攻击场景。实验结果表明，所提方法具有强鲁棒性，即使在仅有少量数据（数据集的5%）被投毒的情况下，仍能提供准确的身份认证。