Demo: Fooling Eavesdroppers via On-Phone Metasurface and Spoofed Audio Information
Wireless eavesdropping on phone conversations has become a major security concern as attackers repurpose advanced wireless capabilities in 5G and beyond featuring higher frequencies and higher sensing resolution. Recent studies have demonstrated that attackers can exploit off-the-shelf millimeter-wave radars to covertly detect even micron-scale vibrations of smartphones caused by the earpiece during the phone conversation, eavesdropping on audio information without the victim ever noticing. In our IEEE S&P'25 paper, we present a new architecture that not only thwarts such attacks but also injects false signatures to fool eavesdroppers into believing they have succeeded. Here, we demonstrate the eavesdropping countermeasure technique that enables the user to hide his private acoustic signals and simultaneously inject an alternative signal via a low-profile, reconfigurable metasurface. We present a metasurface-based audio encoding method that generates artificial audio-vibration signatures to send deceptive audio information to eavesdroppers. We showcase experimental audio samples from both the attack and the proposed countermeasure, which transforms defensive strategies from merely reactive to proactively deceptive.
Authors
- Daniel M. Mittleman (ORCID: https://orcid.org/0000-0003-4277-7419)
- Sadhvikas Addamane (ORCID: https://orcid.org/0000-0003-2958-3767)
- Hou‐Tong Chen (ORCID: https://orcid.org/0000-0003-2014-7571)
- Jy-Chin Liao (ORCID: https://orcid.org/0000-0003-4267-0582)
- Edward W. Knightly (ORCID: https://orcid.org/0000-0002-8663-0438)
- Chun-Chieh Chang (ORCID: https://orcid.org/0000-0003-3728-2454)
- Zhambyl Shaikhanov (ORCID: https://orcid.org/0000-0002-1814-5465)
- Mahmoud Al-Madi (ORCID: https://orcid.org/0000-0001-7922-6309)
Institutions
- Los Alamos National Laboratory (US)
- Brown University (US)
- Sandia National Laboratories (US)
- University of Maryland, College Park (US)
- Rice University (US)
Publication Details
- Published
- 2025-06-27
- DOI
- https://doi.org/10.1145/3734477.3736155
- Citations
- 1
- Primary Topic
- Speech and Audio Processing
- Type
- article
- Field-Weighted Citation Impact
- 1.49