Publications
Increasing the Security of Gaze-Based Cued-Recall Graphical Passwords Using Saliency Masks
Title of Anthology:Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 2012) (to appear)
Publisher:ACM,
Location(s):Austin, Texas
Publication Date:2012
Type of Publication:Article in Collected Edition
Citation:Download RIS
Abstract
With computers being used ever more ubiquitously in situations where privacy is important, secure user authentication is a central requirement. Gaze-based graphical passwords are a particularly promising means for shoulder-surfing-resistant authentication, but selecting secure passwords remains challenging. In this paper, we present a novel gaze-based authentication scheme that makes use of cued-recall graphical passwords on a single image. In order to increase password security, our approach uses a computational model of visual attention to mask those areas of the image that are most likely to attract visual attention. We create a realistic threat model for attacks that may occur in public settings, such as filming the user's interaction while drawing money from an ATM. Based on a 12-participant user study, we show that our approach is significantly more secure than a standard image-based authentication and gaze-based 4-digit PIN entry.
Abstract (secondary)
With computers being used ever more ubiquitously in situations where privacy is important, secure user authentication is a central requirement. Gaze-based graphical passwords are a particularly promising means for shoulder-surfing-resistant authentication, but selecting secure passwords remains challenging. In this paper, we present a novel gaze-based authentication scheme that makes use of cued-recall graphical passwords on a single image. In order to increase password security, our approach uses a computational model of visual attention to mask those areas of the image that are most likely to attract visual attention. We create a realistic threat model for attacks that may occur in public settings, such as filming the user's interaction while drawing money from an ATM. Based on a 12-participant user study, we show that our approach is significantly more secure than a standard image-based authentication and gaze-based 4-digit PIN entry.
