GazeRevealer: Inferring Password Using Smartphone Front Camera

Wang, Y, Cai, W, Gu, T, Shao, W, Khalil, I and Xu, X 2018, 'GazeRevealer: Inferring Password Using Smartphone Front Camera', in Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2018), New York, United States, 5-7 November 2018, pp. 254-263.


Document type: Conference Paper
Collection: Conference Papers

Title GazeRevealer: Inferring Password Using Smartphone Front Camera
Author(s) Wang, Y
Cai, W
Gu, T
Shao, W
Khalil, I
Xu, X
Year 2018
Conference name MobiQuitous '18
Conference location New York, United States
Conference dates 5-7 November 2018
Proceedings title Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2018)
Publisher Association for Computing Machinery
Place of publication New York, United States
Start page 254
End page 263
Total pages 10
Abstract The widespread use of smartphones has brought great convenience to our daily lives, while at the same time we have been increasingly exposed to security threats. Keystroke security is an essential element in user privacy protection. In this paper, we present GazeRe-vealer, a novel side-channel based keystroke inference framework to infer sensitive inputs on smartphone from video recordings of victim's eye patterns captured from smartphone front camera. We observe that eye movements typically follow the keystrokes typing on the number-only soft keyboard during password input. By exploiting eye patterns, we are able to infer the passwords being entered. We propose a novel algorithm to extract sensitive eye pattern images from video streams, and classify different eye patterns with Support Vector Classification. We also propose a novel enhanced method to boost the inference accuracy. Compared with prior key-stroke detection approaches, GazeRevealer does not require any external auxiliary devices, and it relies only on smartphone front camera. We evaluate the performance of GazeRevealer with three different types of smartphones, and the result shows that GazeRe-vealer achieves 77.43% detection accuracy for a single key number and 83.33% inference rate for the 6-digit password in the ideal case.
Subjects Networking and Communications
Mobile Technologies
Ubiquitous Computing
Keyword(s) Password Inference
Gaze Estimation
Mobile Security
DOI - identifier 10.1145/3286978.3287026
Copyright notice © 2018 Association for Computing Machinery
ISBN 9781450360937
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