LidarPhone: Acoustic eavesdropping using a lidar sensor: Poster abstract

Sriram Sami, Sean Rui Xiang Tan, Yimin Dai, Nirupam Roy, Jun Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Private conversations are an attractive target for malicious actors intending to conduct audio eavesdropping attacks. Previous works discovered unexpected vectors for these attacks, such as analyzing high-speed video of objects adjacent to sound sources, or using WiFi signal information. We propose LidarPhone, a novel side-channel attack that exploits the lidar sensors in commodity robot vacuum cleaners to perform acoustic eavesdropping attacks. LidarPhone is able to detect the minute vibrations induced on objects that are near audio sources, and extract meaningful signals from inherently noisy raw lidar returns. We evaluate a realistic scenario for potential victims: recovering privacy-sensitive digits (e.g., credit card numbers, social security numbers) emitted by computer speakers during teleconferencing calls. We implement LidarPhone on a Xiaomi Roborock vacuum cleaning robot and perform a comprehensive series of real-world experiments to determine its performance. LidarPhone achieves up to 91% accuracy for digit classification.

Original languageEnglish
Title of host publicationSenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems
PublisherAssociation for Computing Machinery, Inc
Pages701-702
Number of pages2
ISBN (Electronic)9781450375900
DOIs
Publication statusPublished - 2020 Nov 16
Event18th ACM Conference on Embedded Networked Sensor Systems, SenSys 2020 - Virtual, Online, Japan
Duration: 2020 Nov 162020 Nov 19

Publication series

NameSenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems

Conference

Conference18th ACM Conference on Embedded Networked Sensor Systems, SenSys 2020
Country/TerritoryJapan
CityVirtual, Online
Period20/11/1620/11/19

Bibliographical note

Funding Information:
This research was partially supported by a grant from Singapore Ministry of Education Academic Research Fund Tier 1 (R-252-000-A26-133).

Publisher Copyright:
© 2020 ACM.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Computer Networks and Communications

Fingerprint

Dive into the research topics of 'LidarPhone: Acoustic eavesdropping using a lidar sensor: Poster abstract'. Together they form a unique fingerprint.

Cite this