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

TL;DR

EchoMask is a novel physical-layer system using acoustic metamaterials to anonymize voiceprints in real-time, preventing unauthorized capture without modifying microphones or relying on software.

Contribution

It introduces a practical, low-cost, reconfigurable acoustic metamaterial system for real-time voiceprint anonymization at the physical layer.

Findings

Increases voiceprint Miss-match Rate to over 90% across various environments.

Maintains high speech intelligibility despite interference.

Operates without machine learning, software, or microphone modifications.

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.