On the Vulnerability of Iris-Based Systems to a Software Attack Based on a Genetic Algorithm

  • Marta Gomez-Barrero
  • Javier Galbally
  • Pedro Tome
  • Julian Fierrez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

The vulnerabilities of a standard iris verification system to a novel indirect attack based on a binary genetic algorithm are studied. The experiments are carried out on the iris subcorpus of the publicly available BioSecure DB. The attack has shown a remarkable performance, thus proving the lack of robustness of the tested system to this type of threat. Furthermore, the consistency of the bits of the iris code is analysed, and a second working scenario discarding the fragile bits is then tested as a possible countermeasure against the proposed attack.

Keywords

Security vulnerabilities iris recognition genetic algorithm countermeasures 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Jain, A.K., Ross, A., Pankanti, S.: Biometrics: a tool for information security. IEEE TIFS 1(2), 125–143 (2006)Google Scholar
  2. 2.
    Matsumoto, T.: Gummy finger and paper iris: an update. In: Proc. WISR, pp. 187–192 (2004)Google Scholar
  3. 3.
    Martinez-Diaz, M., Fierrez, J., et al.: An evaluation of indirect attacks and countermeasures in fingerprint verification systems. Pattern Recognition Letters 32, 1643–1651 (2011)CrossRefGoogle Scholar
  4. 4.
    Wei, Z., Qiu, X., et al.: Counterfeit iris detection based on texture analysis. In: Proc. ICPR, pp. 1–4 (2008)Google Scholar
  5. 5.
    Ruiz-Albacete, V., Tome-Gonzalez, P., Alonso-Fernandez, F., Galbally, J., Fierrez, J., Ortega-Garcia, J.: Direct Attacks Using Fake Images in Iris Verification. In: Schouten, B., Juul, N.C., Drygajlo, A., Tistarelli, M. (eds.) BIOID 2008. LNCS, vol. 5372, pp. 181–190. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Soutar, C., Gilroy, R., Stoianov, A.: Biometric system performance and security. In: Proc. IEEE AIAT (1999)Google Scholar
  7. 7.
    Galbally, J., McCool, C., Fierrez, J., Marcel, S.: On the vulnerability of face verification systems to hill-climbing attacks. Pattern Recognition 43, 1027–1038 (2010)MATHCrossRefGoogle Scholar
  8. 8.
    Masek, L., Kovesi, P.: Matlab source code for a biometric identification system based on iris patterns. Master’s thesis, School of Computer Science and Software Engineering, University of Western Australia (2003)Google Scholar
  9. 9.
    Ortega-Garcia, J., Fierrez, J., others: The multi-scenario multi-environment BioSecure multimodal database (BMDB). IEEE TPAMI 32, 1097–1111 (2010)CrossRefGoogle Scholar
  10. 10.
    Daugman, J.: How iris recognition works. IEEE TCSVT 14(1), 21–30 (2004)Google Scholar
  11. 11.
    Daugman, J.: 4. In: Iris Recognition, pp. 71–90. Springer (2008)Google Scholar
  12. 12.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc. (1989)Google Scholar
  13. 13.
    Goldberg, D.: The design of innovation: lessons from and for competent genetic algorithms. Kluwer Academic Publishers (2002)Google Scholar
  14. 14.
    Grother, P., Tabassi, E., Quinn, G.W., Salamon, W.: Irex i: Performance of iris recognition algorithms on standard images. Technical report, National Institute of Standards and Technology (2009)Google Scholar
  15. 15.
    ANSI: Ansi.x9.84 ANSI X9.84-2001, Biometric Information Management and SecurityGoogle Scholar
  16. 16.
    Hollingsworth, K.P., Bowyer, K.W., Flynn, P.J.: The best bits in an iris code. IEEE TPAMI 31(6), 964–973 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marta Gomez-Barrero
    • 1
  • Javier Galbally
    • 1
  • Pedro Tome
    • 1
  • Julian Fierrez
    • 1
  1. 1.Biometric Recognition Group-ATVS, EPSUniversidad Autonoma de MadridMadridSpain

Personalised recommendations