RT Conference Proceedings
T1 Eye-based keystroke prediction for natural texts - a feasibility analysis
A1 Reverte Cazorla, José
A1 Fuentes García-Romero de Tejada, José María de
A1 González Manzano, Lorena
AB The use of videoconferencing is on the rise after COVID-19, being common to look at the screen and see someone typing. A side-channel attack may be launched to infer the text written from the face image. In this paper, we analyse the feasibility of such an attack, being the first proposal which work with a complete keyset (50 keys) and natural texts. We use different scenarios, lighting conditions and natural texts to increase realism. Our study involves 30 participants, who typed 49,365 keystrokes. We characterize the effect of lighting, gender, age and use of glasses. Our results show that on average 13.71%of keystrokes are revealed without error, and up to 31.8%, 52.5% and 61.2% are guessed with a maximum error of 1, 2 and 3 keys, respectively.
PB IEEE
YR 2022
FD 2022-12-09
LK https://hdl.handle.net/10016/36687
UL https://hdl.handle.net/10016/36687
LA eng
NO This work was supported by the Madrid Goverment (Comunidad de Madrid-Spain) under the multianual agreement withUC3M (“fostering young doctor research”, DEPROFAKECM-UC3M) and in the context of the V PRICIT research and technological innovation regional program; by CAM by grant CYNAMON P2018/TCS-4566-CM, cofunded with ERDF; and by Min. of Science and Innovation of Spain by grant ODIO PID2019-111429RB-C21 (AEI/10.13039/501100011033).
DS e-Archivo
RD 30 jun. 2024