Bayesian Hill-Climbing Attack and Its Application to Signature Verification

  • Javier Galbally
  • Julian Fierrez
  • Javier Ortega-Garcia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)


A general hill-climbing attack algorithm based on Bayesian adaption is presented. The approach uses the scores provided by the matcher to adapt a global distribution computed from a development set of users, to the local specificities of the client being attacked. The proposed attack is evaluated on a competitive feature-based signature verification system over the 330 users of the MCYT database. The results show a very high efficiency of the hill-climbing algorithm, which successfully bypassed the system for over 95% of the attacks.


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  1. 1.
    Jain, A.K., Ross, A., Pankanti, S.: Biometrics: a tool for information security. IEEE Trans. on Information Forensics and Security 1, 125–143 (2006)CrossRefGoogle Scholar
  2. 2.
    Ratha, N., Connell, J., Bolle, R.: An analysis of minutiae matching strength. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 223–228. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  3. 3.
    van der Putte, T., Keuning, J.: Biometrical fingerprint recognition: don’t get your fingers burned. In: Proc. IFIP, pp. 289–303 (2000)Google Scholar
  4. 4.
    Galbally, J., Fierrez, J., et al.: On the vulnerability of fingerprint verification systems to fake fingerprint attacks. In: Proc. IEEE of ICCST, pp. 130–136. IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
  5. 5.
    Pacut, A., Czajka, A.: Aliveness detection for iris biometrics. In: Proc. ICCST, pp. 122–129 (2006)Google Scholar
  6. 6.
    Soutar, C.: Biometric system security,
  7. 7.
    Adler, A.: Sample images can be independently restored from face recognition templates. In: Proc. CCECE, vol. 2, pp. 1163–1166 (2003)Google Scholar
  8. 8.
    Uludag, U., Jain, A.K.: Attacks on biometric systems: a case study in fingerprints. In: Proc. SPIE, vol. 5306, pp. 622–633 (2004)Google Scholar
  9. 9.
    Martinez-Diaz, M., Fierrez, J., et al.: Hill-climbing and brute force attacks on biometric systems: a case study in match-on-card fingerprint verification. In: Proc. IEEE of ICCST, pp. 151–159. IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
  10. 10.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, Chichester (2001)MATHGoogle Scholar
  11. 11.
    Ortega-Garcia, J., Fierrez-Aguilar, J., et al.: MCYT baseline corpus: a bimodal biometric database. IEE Proc. Vis. Image Signal Process. 150, 395–401 (2003)CrossRefGoogle Scholar
  12. 12.
    Fierrez-Aguilar, J., Nanni, L., et al.: An on-line signature verification system based on fusion of local and global information. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, Springer, Heidelberg (2005)Google Scholar
  13. 13.
    Jain, A.K., Nandakumar, K., Ross, A.: Score normalization in multimodal biometric systems. Pattern Recognition 38, 2270–2285 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Javier Galbally
    • 1
  • Julian Fierrez
    • 1
  • Javier Ortega-Garcia
    • 1
  1. 1.Biometric Recognition Group–ATVS, EPS, Universidad Autonoma de Madrid, C/ Francisco Tomas y Valiente 11, 28049 MadridSpain

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