用麦克风阵列指纹实现抗环境干扰的语音活体检测
Your Microphone Array Retains Your Identity: A Robust Voice Liveness Detection System for Smart Speakers
- 利用智能音箱麦克风阵列布局生成音频身份指纹
- 在32,780条音频上达到99.84%检测准确率
- 无需用户固定动作，适合真实智能家居场景
尽管在智能家居系统中扮演关键角色，智能音箱易受语音伪造攻击。被动活体检测仅依赖采集音频而非部署传感器，可区分真人发声与回放语音，但受环境变化和用户手势固定的限制。本文提出一种新特征——阵列指纹，利用智能音箱内置麦克风阵列的圆形布局，判断音频来源身份。理论分析表明，相比现有方法，该指纹在环境变化和用户移动下更具鲁棒性。为此，我们设计了轻量级方案ARRAYID，结合一系列协同特征。在包含32,780个音频样本和14种伪造设备的数据集上，ARRAYID达到99.84%的准确率，显著优于现有被动活体检测方案。
原文摘要 · Abstract (English)
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.
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