Your Microphone Array Retains Your Identity: A Robust Voice Liveness Detection System for Smart Speakers

TL;DR

This paper introduces ARRAYID, a robust voice liveness detection system for smart speakers that leverages microphone array fingerprints to accurately distinguish live voices from replayed ones, even under environmental changes.

Contribution

It proposes a novel array fingerprint feature based on microphone layout and a lightweight detection scheme, improving robustness and accuracy over existing methods.

Findings

Achieved 99.84% accuracy on a large dataset

Demonstrated robustness under environmental and user movement variations

Outperformed existing passive liveness detection schemes

Abstract

Though playing an essential role in smart home systems, smart speakers are vulnerable to voice spoofing attacks. Passive liveness detection, which utilizes only the collected audio rather than the deployed sensors to distinguish between live-human and replayed voices, has drawn increasing attention. However, it faces the challenge of performance degradation under the different environmental factors as well as the strict requirement of the fixed user gestures. In this study, we propose a novel liveness feature, array fingerprint, which utilizes the microphone array inherently adopted by the smart speaker to determine the identity of collected audios. Our theoretical analysis demonstrates that by leveraging the circular layout of microphones, compared with existing schemes, array fingerprint achieves a more robust performance under the environmental change and user's movement. Then, to leverage such a fingerprint, we propose ARRAYID, a lightweight passive detection scheme, and elaborate a series of features working together with array fingerprint. Our evaluation on the dataset containing 32,780 audio samples and 14 spoofing devices shows that ARRAYID achieves an accuracy of 99.84%, which is superior to existing passive liveness detection schemes.