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Generation and Evaluation of Brute-Force Signature Forgeries

  • Alain Wahl
  • Jean Hennebert
  • Andreas Humm
  • Rolf Ingold
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4105)

Abstract

We present a procedure to create brute-force signature forgeries. The procedure is supported by Sign4J, a dynamic signature imitation training software that was specifically built to help people learn to imitate the dynamics of signatures. The main novelty of the procedure lies in a feedback mechanism that is provided to let the user know how good the imitation is and on what part of the signature the user has still to improve. The procedure and the software are used to generate a set of brute-force signatures on the MCYT-100 database. This set of forged signatures is used to evaluate the rejection performance of a baseline dynamic signature verification system. As expected, the brute-force forgeries generate more false acceptation in comparison to the random and low-force forgeries available in the MCYT-100 database.

Keywords

Gaussian Mixture Model Original Signature Equal Error Rate Dynamic Signature Local Threshold 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of Royal Statistical Society 39(1), 1–38 (1977)MATHMathSciNetGoogle Scholar
  2. 2.
    Humm, A., Hennebert, J., Ingold, R.: Gaussian mixture models for chasm signature verification. In: Accepted for publication in 3rd Joint Workshop on Multimodal Interaction and Related Machine Learning Algorithms, Washington (2006)Google Scholar
  3. 3.
    Leclerc, F., Plamondon, R.: Automatic signature verification: the state of the art–1989-1993. Int’l. J. Pattern Recognition and Artificial Intelligence 8(3), 643–660 (1994)CrossRefGoogle Scholar
  4. 4.
    Ly Van, B., Garcia-Salicetti, S., Dorizzi, B.: Fusion of hmm’s likelihood and viterbi path for on-line signature verification. In: Biometrics Authentication Workshop, Prague (May 15, 2004)Google Scholar
  5. 5.
    Ortega-Garcia, J., Fierrez-Aguilar, J., Simon, D., Gonzalez, J., Faundez-Zanuy, M., Espinosa, V., Satue, A., Hernaez, I., Igarza, J.-J., Vivaracho, C., Escudero, D., Moro, Q.-I.: Mcyt baseline corpus: a bimodal biometric database. In: IEE Proc.-Vis. Image Signal Process, vol. 150(6), pp. 395–401 (2003)Google Scholar
  6. 6.
    Plamondon, R., Lorette, G.: Automatic signature verification and writer identification - the state of the art. Pattern Recognition 22(2), 107–131 (1989)CrossRefGoogle Scholar
  7. 7.
    Richiardi, J., Drygajlo, A.: Gaussian mixture models for on-line signature verification. In: Proc. 2003 ACM SIGMM workshop on Biometrics methods and applications, pp. 115–122 (2003)Google Scholar
  8. 8.
    Vielhauer, C.: Biometric User Authentication for IT Security. Springer, Heidelberg (2006)Google Scholar
  9. 9.
    Zoebisch, F., Vielhauer, C.: A test tool to support brut-force online and offline signature forgery tests on mobile devices. In: Proceedings of the IEEE International Conference on Multimedia and Expo. 2003 (ICME), vol. 3, pp. 225–228 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Alain Wahl
    • 1
  • Jean Hennebert
    • 1
  • Andreas Humm
    • 1
  • Rolf Ingold
    • 1
  1. 1.Université de FribourgFribourgSwitzerland

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