论文标题
通用对抗性扰动生成网络，用于说话者识别
Universal Adversarial Perturbations Generative Network for Speaker Recognition
论文作者
论文摘要
鉴于神经网络容易受到对抗性示例的影响，基于攻击深度学习的生物识别系统已通过指纹/面部/说话者识别系统的广泛部署吸引了越来越多的关注。在本文中，我们证明了说话者识别系统的普遍对抗扰动〜（UAPS）的存在。我们提出了一个生成网络，以学习从低维正态分布到UAPS子空间的映射，然后合成UAPS以perturbe任何输入信号，以欺骗训练有素的说话者识别模型，具有很高的可能性。关于TimIt和Librispeech数据集的实验结果证明了我们模型的有效性。
Attacking deep learning based biometric systems has drawn more and more attention with the wide deployment of fingerprint/face/speaker recognition systems, given the fact that the neural networks are vulnerable to the adversarial examples, which have been intentionally perturbed to remain almost imperceptible for human. In this paper, we demonstrated the existence of the universal adversarial perturbations~(UAPs) for the speaker recognition systems. We proposed a generative network to learn the mapping from the low-dimensional normal distribution to the UAPs subspace, then synthesize the UAPs to perturbe any input signals to spoof the well-trained speaker recognition model with high probability. Experimental results on TIMIT and LibriSpeech datasets demonstrate the effectiveness of our model.
