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Eye-based keystroke prediction for natural texts - a feasibility analysis

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: COSEC (Computer SECurity Lab)
dc.contributor.authorReverte Cazorla, José
dc.contributor.authorFuentes García-Romero de Tejada, José María de
dc.contributor.authorGonzález Manzano, Lorena
dc.contributor.funderComunidad de Madrides
dc.contributor.funderAgencia Estatal de Investigación (España)es
dc.contributor.funderUniversidad Carlos III de Madrides
dc.date.accessioned2023-02-28T08:26:17Z
dc.date.available2023-02-28T08:26:17Z
dc.date.issued2022-12-09
dc.description.abstractThe 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.en
dc.description.sponsorshipThis work was supported by the Madrid Goverment (Comunidad de Madrid-Spain) under the multianual agreement with UC3M (“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).en
dc.identifier.bibliographicCitationJ. Reverte Cazorla, J.M. de Fuentes and L. González Manzano, "Eye-based keystroke prediction for natural texts - a feasibility analysis", presentada en 21st IEEE International Conference on Trust, Security and Privacy in Computing and Communication (TrustCom 2022), Wuhan, 9-11 dec., 2022.en
dc.identifier.publicationfirstpage1
dc.identifier.publicationlastpage8
dc.identifier.urihttps://hdl.handle.net/10016/36687
dc.identifier.uxxiCC/0000033721
dc.language.isoeng
dc.publisherIEEEen
dc.relation.eventdate2022-12-09
dc.relation.eventplaceCHINAes
dc.relation.eventtitleThe 21st IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2022)en
dc.relation.projectIDGobierno de España. PID2019-111429RB-C21es
dc.rights©2022 IEEE.es
dc.rights.accessRightsopen accessen
dc.subject.ecienciaInformáticaes
dc.subject.otherkeystrokeen
dc.subject.othereye trackingen
dc.subject.otherpredictionen
dc.titleEye-based keystroke prediction for natural texts - a feasibility analysisen
dc.typeconference output*
dc.type.hasVersionAM*
dspace.entity.typePublication
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