Poster: Extracting Speech from Subtle Room Object Vibrations Using Remote mmWave Sensing

Speech privacy leakage has long been a public concern. Existing non-microphone-based eavesdropping attacks rely on physical contact or line-of-sight between the sensor (e.g., a motion sensor or a radar) and the victim sound source. In this poster, we investigate a new form of attack that remotely elicits speech from minute surface vibrations upon common room objects (e.g., paper bags, plastic storage bin) via mmWave sensing. We design and implement a high-resolution software-defined phased-MIMO radar that integrates transmit beamforming, virtual array, and receive beamforming. The proposed system enhances sensing directivity by focusing all the mmWave beams toward a target room object. We successfully demonstrate such an attack by developing a deep speech recognition scheme grounded on unsupervised domain adaptation. Without prior training on the victim's data, our attack can achieve a high success rate of over 90% in recognizing simple digits.