Speech privacy attack via vibrations from room objects leveraging a phased-MIMO radar

Speech privacy leakage has long been a public concern. Through speech eavesdropping, an adversary may steal a user's private information or an enterprise's financial/intellectual properties, leading to catastrophic consequences. 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 discover a new form of speech eavesdropping attack that senses minor speech-induced vibrations upon common room objects using mmWave. By integrating phasedarray and multiple-input and multiple-output (MIMO) on a single mmWave transceiver, our attack can capture and fuse micrometerlevel vibrations upon the surfaces of multiple objects to reveal speech content in a remote and non-line-of-sight fashion. 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 speech content.