TY - GEN
T1 - An eye gaze-driven metric for estimating the strength of graphical passwords based on image hotspots
AU - Constantinides, Argyris
AU - Belk, Marios
AU - Fidas, Christos
AU - Pitsillides, Andreas
N1 - Publisher Copyright:
© ACM.
PY - 2020/3/17
Y1 - 2020/3/17
N2 - In this paper, we propose an eye gaze-driven metric based on hotspot vs. non-hotspot segments of images for unobtrusively estimating the strength of user-created graphical passwords by analyzing the users' eye gaze behavior during password creation. To examine the feasibility of this method, i.e., the existence of correlation between the proposed metric and the strength of users' generated passwords, we conducted an eye-tracking study (n=42), in which users created a graphical password with a personalized image that triggers declarative memory of users (familiar image) vs. an image illustrating generic content unfamiliar to the users' episodic and semantic memory (generic image). Results revealed a strong positive correlation between the password strength and the proposed eye gaze-driven metric, pointing towards a new direction for the design of intelligent eye gaze-driven graphical password schemes for unobtrusively assisting users in making better password choices.
AB - In this paper, we propose an eye gaze-driven metric based on hotspot vs. non-hotspot segments of images for unobtrusively estimating the strength of user-created graphical passwords by analyzing the users' eye gaze behavior during password creation. To examine the feasibility of this method, i.e., the existence of correlation between the proposed metric and the strength of users' generated passwords, we conducted an eye-tracking study (n=42), in which users created a graphical password with a personalized image that triggers declarative memory of users (familiar image) vs. an image illustrating generic content unfamiliar to the users' episodic and semantic memory (generic image). Results revealed a strong positive correlation between the password strength and the proposed eye gaze-driven metric, pointing towards a new direction for the design of intelligent eye gaze-driven graphical password schemes for unobtrusively assisting users in making better password choices.
KW - entropy
KW - eye-tracking
KW - graphical passwords
KW - security
KW - user authentication
UR - http://www.scopus.com/inward/record.url?scp=85082505986&partnerID=8YFLogxK
U2 - 10.1145/3377325.3377537
DO - 10.1145/3377325.3377537
M3 - Conference contribution
AN - SCOPUS:85082505986
T3 - International Conference on Intelligent User Interfaces, Proceedings IUI
SP - 33
EP - 37
BT - Proceedings of the 25th International Conference on Intelligent User Interfaces, IUI 2020
PB - Association for Computing Machinery
T2 - 25th ACM International Conference on Intelligent User Interfaces, IUI 2020
Y2 - 17 March 2020 through 20 March 2020
ER -