MILLIEAR: Millimeter-wave Acoustic Eavesdropping with Unconstrained Vocabulary
MILLIEAR: Millimeter-wave Acoustic Eavesdropping with Unconstrained Vocabulary
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DOI：
10.1109/infocom48880.2022.9796940
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Pengfei Hu;Yifan Ma;P. Santhalingam;Parth H. Pathak;Xiuzhen Cheng
中科院分区：
文献类型：
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作者：
Pengfei Hu;Yifan Ma;P. Santhalingam;Parth H. Pathak;Xiuzhen Cheng
随着声学通信系统在家庭和办公室中变得越来越普遍，窃听带来了重大的安全和隐私风险。目前的声学窃听方法要么由于使用低于6 GHz的频率而提供低分辨率，要么仅对使用分类的有限单词起作用，要么由于使用光学传感器而不能穿墙工作。在本文中，我们介绍了毫米波声学窃听系统MILLIQUIS，该系统利用毫米波FMCW测距和生成机器学习模型的高分辨率，不仅可以提取振动，还可以重建音频。MILLIQUID将说话人振动估计与条件生成对抗网络相结合，以不受约束的词汇进行窃听。我们使用部署在不同场景和设置中的现成毫米波雷达来实现和评估MIL-LIQUID。我们发现，它可以准确地重建音频，即使在不同的距离，角度和通过不同的绝缘体材料的墙壁。我们的主观和客观评价表明，重建的音频与原始音频有很强的相似性。
As acoustic communication systems become more common in homes and offices, eavesdropping brings significant security and privacy risks. Current approaches of acoustic eavesdropping either provide low resolution due to the use of sub-6 GHz frequencies, work only for limited words using classification, or cannot work through-wall due to the use of optical sensors. In this paper, we present MILLIEAR, a mmWave acoustic eavesdropping system that leverages the high-resolution of mmWave FMCW ranging and generative machine learning models to not only extract vibrations but to reconstruct the audio. MILLIEAR combines speaker vibration estimation with conditional generative adversarial networks to eavesdrop with unconstrained vocabulary. We implement and evaluate MIL-LIEAR using off-the-shelf mmWave radar deployed in different scenarios and settings. We find that it can accurately reconstruct the audio even at different distances, angles and through the wall with different insulator materials. Our subjective and objective evaluations show that the reconstructed audio has a strong similarity with the original audio.