说话人识别
计算机科学
稳健性(进化)
对手
语音识别
对抗制
说话人验证
深层神经网络
人气
人工神经网络
人工智能
计算机安全
生物化学
社会心理学
基因
心理学
化学
作者
Yi Xie,Cong Shi,Zhuohang Li,Jian Liu,Yingying Chen,Bo Yuan
标识
DOI:10.1109/icassp40776.2020.9053747
摘要
As the popularity of voice user interface (VUI) exploded in recent years, speaker recognition system has emerged as an important medium of identifying a speaker in many security-required applications and services. In this paper, we propose the first real-time, universal, and robust adversarial attack against the state-of-the-art deep neural network (DNN) based speaker recognition system. Through adding an audio-agnostic universal perturbation on arbitrary enrolled speaker's voice input, the DNN-based speaker recognition system would identify the speaker as any target (i.e., adversary-desired) speaker label. In addition, we improve the robustness of our attack by modeling the sound distortions caused by the physical over-the-air propagation through estimating room impulse response (RIR). Experiment using a public dataset of 109 English speakers demonstrates the effectiveness and robustness of our proposed attack with a high attack success rate of over 90%. The attack launching time also achieves a 100× speedup over contemporary non-universal attacks.
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