Acoustic Eavesdropping From Sound-Induced Vibrations With Multi-Antenna mmWave Radar

Wenhao Li, Riccardo Spolaor, Chuanwen Luo, Yuchao Sun, Huashan Chen, Guoming Zhang, Yanni Yang, Xiuzhen Cheng, Pengfei Hu

Abstract: Acoustic eavesdropping against private or confidential spaces is a significant threat in the realm of privacy protection. While the presence of soundproof material would weaken such an attack, current eavesdropping technology may be able to bypass these protections. Fortunately, existing studies either inadequately cover the full spectrum of human speech due to low-frequency responses or rely heavily on the prior knowledge used to train a model. To address these challenges, this paper introduces mmEcho, a new acoustic eavesdropping method that utilizes millimeter-wave signals to sense vibration induced by sound precisely. Through signal processing techniques such as the intra-chirp scheme and phase calibration algorithm, mmEcho achieves micrometer-level vibration extraction without requiring target-related data. To improve the range of eavesdropping attacks while reducing noise, we optimize radar signals by leveraging the widespread availability of multiple antennas on commercial off-the-shelf radars. We comprehensively evaluate the performance of mmEcho in different real-world settings. Experimental results demonstrate that, with the aid of multi-antenna technology, mmEcho can more effectively reconstruct the audio from the target at various distances, directions, sound insulators, reverberating objects, sound levels, and languages. Compared to existing methods, our approach provides better effectiveness without prior knowledge, such as the speech data from the target.

External IDs:dblp:journals/tmc/LiSLSCZYCH25