Journal
IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (IEEE INFOCOM 2022)
Volume -, Issue -, Pages 11-20Publisher
IEEE
DOI: 10.1109/INFOCOM48880.2022.9796940
Keywords
-
Categories
Funding
- National Key Research and Development Program of China [2021YFB3100400]
Ask authors/readers for more resources
As acoustic communication systems become more common, eavesdropping poses significant security risks. This paper presents MILLIEAR, a mmWave acoustic eavesdropping system that extracts vibrations and reconstructs audio using generative machine learning models, achieving unconstrained vocabulary eavesdropping.
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 MILLIEAR 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available