A Genetic Algorithm Enabled Similarity-Based Attack on Cancellable Biometrics

Xingbo Dong, Zhe Jin, Andrew Teoh Beng Jin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Cancellable biometrics (CB) as a means for biometric template protection approach refers to an irreversible yet similarity preserving transformation on the original template. With similarity preserving property, the matching between template and query instance can be performed in the transform domain without jeopardizing accuracy performance. Unfortunately, this trait invites a class of attack, namely similarity-based attack (SA). SA produces a preimage, an inverse of transformed template, which can be exploited for impersonation and cross-matching. In this paper, we propose a Genetic Algorithm enabled similarity-based attack framework (GASAF) to demonstrate that CB schemes whose possess similarity preserving property are highly vulnerable to similarity-based attack. Besides that, a set of new metrics is designed to measure the effectiveness of the similarity-based attack. We conduct the experiment on two representative CB schemes, i.e. BioHashing and Bloom-filter. The experimental results attest the vulnerability under this type of attack.

Original languageEnglish
Title of host publication2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems, BTAS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728115221
DOIs
Publication statusPublished - 2019 Sep
Event10th IEEE International Conference on Biometrics Theory, Applications and Systems, BTAS 2019 - Tampa, United States
Duration: 2019 Sep 232019 Sep 26

Publication series

Name2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems, BTAS 2019

Conference

Conference10th IEEE International Conference on Biometrics Theory, Applications and Systems, BTAS 2019
CountryUnited States
CityTampa
Period19/9/2319/9/26

Bibliographical note

Funding Information:
This research was partly supported by Fundamental Research Grant Scheme (FRGS/1/2018/ICT02/MUSM/03/3). We also gratefully acknowledge the support of NVIDIA Corporation with the GPU grant donation of the Titan Xp GPU used for this research. Thanks for the helpful discus-

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Software
  • Instrumentation

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