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Daniel P. Lopresti

“The Effectiveness of Generative Attacks on an Online Handwriting Biometric” (with J. Raim), Proceedings of the Fifth International Conference on Audio- and Video-based Biometric Person Authentication, T. Kanade and A. K. Jain and N. K. Ratha, eds., Berlin:  Springer-Verlag, 2005, pp. 1090-1099.

The traditional approach to evaluating the performance of a behavioral biometric such as handwriting or speech is to conduct a study involving human subjects (naive and/or skilled "forgers") and report the system's False Reject Rate (FRR) and False Accept Rate (FAR). In this paper, we examine a different and perhaps more ominous threat: the possibility that the attacker has access to a generative model for the behavior in question, along with information gleaned about the targeted user, and can employ this in a methodical search of the space of possible inputs to the system in an attempt to break the biometric. We present preliminary experimental results examining the effectiveness of this line of attack against a published technique for constructing a biometric hash based on online handwriting data. Using a concatenative approach followed by a feature space search, our attack succeeded 49% of the time.

Paper  (PDF 254 kbytes)

© 2004 P.C. Rossin College of Engineering & Applied Science
Computer Science & Engineering, Packard Laboratory, Lehigh University, Bethlehem PA 18015