Speaker verification system has gained great popularity in recent years, especially with the development of deep neural networks and Internet of Things. However, the security of speaker verification system based on deep neural networks has not been well investigated. In this paper, we propose an attack to spoof the state-of-the-art speaker verification system based on generalized end-to-end (GE2E) loss function for misclassifying illegal users into the authentic user. Specifically, we design a novel loss function to deploy a generator for generating effective adversarial examples with slight perturbation and then spoof the system with these adversarial examples to achieve our goals. The success rate of our attack can reach 82% when cosine similarity is adopted to deploy the deep-learning-based speaker verification system. Beyond that, our experiments also reported the signal-to-noise ratio at 76 dB, which proves that our attack has higher imperceptibility than previous works. In summary, the results show that our attack not only can spoof the state-of-the-art neural-network-based speaker verification system but also more importantly has the ability to hide from human hearing or machine discrimination.

中文翻译：

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利用广义说话人差异通过对抗性例子欺骗说话人验证系统

说话者验证系统近年来尤其受到深度神经网络和物联网的发展而获得了极大的普及。但是，基于深度神经网络的说话人验证系统的安全性尚未得到很好的研究。在本文中，我们提出了一种攻击，以欺骗基于通用端到端（GE2E）丢失功能的最新说话者验证系统，从而将非法用户误分类为真实用户。具体而言，我们设计了一种新颖的损失函数，以部署生成器以生成具有轻微扰动的有效对抗示例，然后使用这些对抗示例欺骗系统以实现我们的目标。当采用余弦相似度来部署基于深度学习的说话者验证系统时，我们的攻击成功率可以达到82％。除此之外，我们的实验还报告了76 dB的信噪比，这证明我们的攻击比以前的工作具有更高的不可察觉性。总而言之，结果表明，我们的攻击不仅可以欺骗基于神经网络的最新说话人验证系统，而且更重要的是，它具有对人类听觉或机器辨别力隐藏的能力。