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short-paper

Protecting from Lunchtime Attack Using an Uncalibrated Eye Tracker Signal

Authors:

Pawel Kasprowski,

Katarzyna HarezlakAuthors Info & Claims

ETRA '20 Short Papers: ACM Symposium on Eye Tracking Research and Applications

Article No.: 6, Pages 1 - 5

https://doi.org/10.1145/3379156.3391348

Published: 02 June 2020 Publication History

Abstract

Eye movement-based biometric has been developed for over 15 years, but for now - to the authors’ knowledge - no commercial applications utilize this modality. There are many reasons for this, starting from still low accuracy and ending with the problematic setup. One of the essential elements of this setup is the calibration, as nearly every eye tracker needs to be calibrated before its first usage. This procedure makes any authentication based on eye movement a cumbersome and lengthy process.

The main idea of the research presented in this paper is to perform authentication based on a signal from a cheap remote eye tracker but - contrary to the previous studies - without any calibration of the device. The uncalibrated signal obtained from the eye tracker is used directly, which significantly simplifies the enrollment process.

The experiment presented in the paper aims at protection from a so-called ”lunchtime attack” when an unauthorized person starts using a computer, taking advantage of the absence of the legitimate user. We show that such an impostor may be detected with an analysis of the signal obtained from the eye tracker when the user clicks with a mouse objects on a screen. The method utilizes the assumptions that: (1) users usually look at the point they click, and (2) an uncalibrated eye tracker signal is different for different users.

It has been shown that after the analysis of nine subsequent clicks, the method is able to achieve the Equal Error Rate lower than 15% and may be treated as a valuable and difficult to counterfeit supplement to classic face recognition and password-based computer protection methods.

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Cited By

Tanaka TRampazzi SSugawara T(2024)LightTouch: Harnessing Laser-Based Signal Injection to Manipulate Optical Human-Computer InterfacesIEEE Access10.1109/ACCESS.2024.341357112(84033-84045)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3413571
Odya PGórski FCzyżewski A(2023)User Authentication by Eye Movement Features Employing SVM and XGBoost ClassifiersIEEE Access10.1109/ACCESS.2023.330900011(93341-93353)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3309000

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Published In

cover image ACM Conferences

ETRA '20 Short Papers: ACM Symposium on Eye Tracking Research and Applications

June 2020

305 pages

ISBN:9781450371346

DOI:10.1145/3379156

Editors:
Andreas Bulling
University of Stuttgart, Germany
,
Anke Huckauf
Ulm University, Germany
,
Eakta Jain
University of Florida, USA
,
Ralph Radach
University of Wuppertal, Germany
,
Daniel Weiskopf
University of Stuttgart, Germany

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 02 June 2020

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Politechnika ?l?ska

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ETRA '20

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ETRA '20: 2020 Symposium on Eye Tracking Research and Applications

June 2 - 5, 2020

Stuttgart, Germany

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Cited By

Tanaka TRampazzi SSugawara T(2024)LightTouch: Harnessing Laser-Based Signal Injection to Manipulate Optical Human-Computer InterfacesIEEE Access10.1109/ACCESS.2024.341357112(84033-84045)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3413571
Odya PGórski FCzyżewski A(2023)User Authentication by Eye Movement Features Employing SVM and XGBoost ClassifiersIEEE Access10.1109/ACCESS.2023.330900011(93341-93353)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3309000

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