用声学超材料在物理层隐藏语音特征，防窃听且无需供电
Before the Mic: Physical-Layer Voiceprint Anonymization with Acoustic Metamaterials
- 通过可调声学结构干扰语音频段，保留可懂度同时破坏指纹特征
- 实测在8种麦克风上使匹配失败率超90%，语音可懂度保持良好
- 3D打印、无电、不依赖软件，适合会议、访谈等高风险场景
语音指纹广泛用于身份认证，但在公共场合极易被截获，一旦泄露无法撤销。现有匿名系统依赖录音设备内部运行，当麦克风或软件不可信时（如会议室、讲座厅、访谈现场）失效。本文提出EchoMask，首个基于声学超材料的实时物理层语音指纹匿名系统。通过在声波到达麦克风前进行干预，阻止攻击者通过被控设备获取清晰语音指纹。设计包含三大创新：选择性频率干扰，在破坏语音指纹特征的同时保持语音可懂度；声场模型确保说话人移动时系统稳定；可重构结构产生随时间变化的干扰，防止学习或抵消固定声学模式。EchoMask成本低、无需供电、可3D打印，不依赖机器学习、软件支持或麦克风改造。在八种麦克风、多种环境下的实验表明，其使匹配失败率（即误配率）超过90%，同时保持高语音可懂度。
原文摘要 · Abstract (English)
Voiceprints are widely used for authentication; however, they are easily captured in public settings and cannot be revoked once leaked. Existing anonymization systems operate inside recording devices, which makes them ineffective when microphones or software are untrusted, as in conference rooms, lecture halls, and interviews. We present EchoMask, the first practical physical-layer system for real-time voiceprint anonymization using acoustic metamaterials. By modifying sound waves before they reach the microphone, EchoMask prevents attackers from capturing clean voiceprints through compromised devices. Our design combines three key innovations: frequency-selective interference to disrupt voiceprint features while preserving speech intelligibility, an acoustic-field model to ensure stability under speaker movement, and reconfigurable structures that create time-varying interference to prevent learning or canceling a fixed acoustic pattern. EchoMask is low-cost, power-free, and 3D-printable, requiring no machine learning, software support, or microphone modification. Experiments conducted across eight microphones in diverse environments demonstrate that EchoMask increases the Miss-match Rate, i.e., the fraction of failed voiceprint matching attempts, to over 90%, while maintaining high speech intelligibility.
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