Morph-a-Dope: Using Pupil Manipulation to Spoof Eye Movement Biometrics

Isaac Griswold-Steiner, Zakery Fyke, Mushfique Ahmed, Abdul Serwadda

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Eye Tracking Authentication - a mechanism where eye movement patterns are used to verify a user's identity - is increasingly being explored for use as a layer of security in computing systems. Despite being widely studied, there is barely any research investigating how these systems could be attacked by a determined attacker. In particular, the relationship between pupil characteristics and lighting is one that could lead to vulnerabilities in improperly secured systems.This paper presents Morph-a-Dope, an attack that leverages lighting manipulations to defeat eye tracking authentication systems that heavily rely on features derived from pupil sizes. Across 20 attacker-victim pairs, the attack increased the EER by an average of over 50% as compared to the zero-effort attack by the overall population, and as much as 500% for individual victims. Our research calls for a greater emphasis on manipulation-resistant pupil size features or system designs that otherwise avoid such vulnerabilities.

Original languageEnglish
Title of host publication2018 9th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2018
EditorsSatyajit Chakrabarti, Himadri Nath Saha
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages543-552
Number of pages10
ISBN (Electronic)9781538676936
DOIs
StatePublished - Nov 2018
Event9th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2018 - New York City, United States
Duration: Nov 8 2018Nov 10 2018

Publication series

Name2018 9th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2018

Conference

Conference9th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2018
CountryUnited States
CityNew York City
Period11/8/1811/10/18

Keywords

  • behavioral biometrics
  • continuous authentication
  • eye tracking biometrics
  • machine learning
  • spoof attack

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