School of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China.
Gansu Province Internet of Things Engineering Research Centre, Northwest Normal University, Lanzhou 730070, China.
Sensors (Basel). 2022 Nov 29;22(23):9309. doi: 10.3390/s22239309.
With the increasing popularity of smart devices, users can control their mobile phones, TVs, cars, and smart furniture by using voice assistants, but voice assistants are susceptible to intrusion by outsider speakers or playback attacks. In order to address this security issue, a millimeter-wave radar-based voice security authentication system is proposed in this paper. First, the speaker's fine-grained vocal cord vibration signal is extracted by eliminating static object clutter and motion effects; second, the weighted Mel Frequency Cepstrum Coefficients (MFCCs) are obtained as biometric features; and finally, text-independent security authentication is performed by the WMHS (Weighted MFCCs and Hog-based SVM) method. This system is highly adaptable and can authenticate designated speakers, resist intrusion by other unspecified speakers as well as playback attacks, and is secure for smart devices. Extensive experiments have verified that the system achieves a 93.4% speaker verification accuracy and a 5.8% miss detection rate for playback attacks.
随着智能设备的普及，用户可以通过语音助手控制手机、电视、汽车和智能家居，但语音助手容易受到外部扬声器或回放攻击的入侵。为了解决这个安全问题，本文提出了一种基于毫米波雷达的语音安全认证系统。首先，通过消除静态物体干扰和运动效应提取说话人的精细声带振动信号；其次，得到作为生物特征的加权梅尔频率倒谱系数（MFCCs）；最后，通过 WMHS（加权 MFCCs 和基于 Hog 的 SVM）方法进行文本无关的安全认证。该系统具有高度的适应性，能够认证指定的说话人，抵抗其他未指定的说话人的入侵和回放攻击，对智能设备是安全的。大量实验验证了该系统在回放攻击时的说话人验证准确率达到 93.4%，误检率为 5.8%。
