VocalPrint: exploring a resilient and secure voice authentication via mmWave biometric interrogation
VocalPrint: exploring a resilient and secure voice authentication via mmWave biometric interrogation
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DOI：
10.1145/3384419.3430779
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发表时间：
2020-11
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通讯作者：
Huining Li;Chenhan Xu;Aditya Singh Rathore;Zhengxiong Li;Hanbin Zhang;Chen Song;Kun Wang;Lu Su;Feng Lin;K. Ren;Wenyao Xu
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文献类型：
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作者：
Huining Li;Chenhan Xu;Aditya Singh Rathore;Zhengxiong Li;Hanbin Zhang;Chen Song;Kun Wang;Lu Su;Feng Lin;K. Ren;Wenyao Xu
随着语音控制设备的持续增长，语音度量已被广泛用于用户识别。然而，语音生物识别技术很容易受到重播攻击和环境噪音的影响。我们发现，语音生物识别技术的根本脆弱性根源于其间接感测模式（例如，麦克风）。在本文中，我们提出了VocalPrint，一个弹性毫米波询问系统，直接捕获和分析的声音振动的用户身份验证。具体而言，VocalPrint利用了用户近喉咙区域周围的皮肤反射射频（RF）信号的独特干扰，这是由通信期间的声音振动引起的。复杂的环境噪声是从RF信号中分离出来的，使用一种新的连续性感知杂波抑制方法来保持细粒度的声乐生物特征。然后，我们提取与文本无关的声道和声源特征，并将其输入到集成分类器进行用户认证。VocalPrint是实用的，因为它利用低成本，便携式和节能的硬件，允许轻松地过渡到智能手机，同时由于其非接触性质，具有足够的可用性作为典型的语音认证系统。我们的实验结果从41名参与者不同的审讯距离，方向和身体运动表明，VocalPrint可以实现超过96%的认证准确率，即使在不利的条件下。我们展示了我们的系统对复杂的噪声干扰和各种威胁级别的欺骗攻击的弹性。
With the continuing growth of voice-controlled devices, voice metrics have been widely used for user identification. However, voice biometrics is vulnerable to replay attacks and ambient noise. We identify that the fundamental vulnerability in voice biometrics is rooted in its indirect sensing modality (e.g., microphone). In this paper, we present VocalPrint, a resilient mmWave interrogation system which directly captures and analyzes the vocal vibrations for user authentication. Specifically, VocalPrint exploits the unique disturbance of the skin-reflect radio frequency (RF) signals around the near-throat region of the user, caused by the vocal vibrations during communication. The complex ambient noise is isolated from the RF signal using a novel resilience-aware clutter suppression approach for preserving fine-grained vocal biometric properties. Afterward, we extract the text-independent vocal tract and vocal source features and input them to an ensemble classifier for user authentication. VocalPrint is practical as it leverages a low-cost, portable, and energy-efficient hardware allowing effortless transition to a smartphone while having sufficient usability as typical voice authentication systems due to its non-contact nature. Our experimental results from 41 participants with different interrogation distances, orientations, and body motions show that VocalPrint can achieve over 96% authentication accuracy even under unfavorable conditions. We demonstrate the resilience of our system against complex noise interference and spoof attacks of various threat levels.