Before the Mic: Physical-Layer Voiceprint Anonymization with Acoustic Metamaterials

2026-04-21

AI Summary

This work proposes EchoMask, the first physical-layer, real-time voice anonymization system based on acoustic metamaterials, addressing the vulnerability of existing approaches that rely on device-side processing and fail when microphones or software are untrusted. EchoMask prevents clear voiceprint leakage at the source by introducing frequency-selective acoustic interference before sound reaches the microphone. Integrating acoustic field stability modeling with 3D-printed reconfigurable structures, the system achieves strong anonymity and high speech intelligibility without requiring machine learning, software intervention, or hardware modifications. It also exhibits robustness against adversarial learning and cancellation attacks. Experimental results across eight microphone types and diverse environments demonstrate a voiceprint matching failure rate exceeding 90%, significantly outperforming current methods.

Technology Category

• Machine Learning: Privacy
• Natural Language Processing: Speech
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• voiceprint anonymization
• acoustic metamaterials
• physical-layer security
• privacy protection
• untrusted microphones

Innovation

• acoustic metamaterials
• voiceprint anonymization
• physical-layer security
• frequency-selective interference
• reconfigurable structures