A conceptual framework for testing biometric algorithms within operating systems' authentication
Proceedings of the 2002 ACM symposium on Applied computing
Toward Speech-Generated Cryptographic Keys on Resource-Constrained Devices
Proceedings of the 11th USENIX Security Symposium
Biometric Hash based on Statistical Features of Online Signatures
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Transitivity Based Enrollment Strategy for Signature Verification Systems
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
A test tool to support brute-force online and offline signature forgery tests on mobile devices
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
From Scores to Face Templates: A Model-Based Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human-seeded attacks and exploiting hot-spots in graphical passwords
SS'07 Proceedings of 16th USENIX Security Symposium on USENIX Security Symposium
The practical subtleties of biometric key generation
SS'08 Proceedings of the 17th conference on Security symposium
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The traditional approach to evaluating the performance of a behavioral biometric such as handwriting or speech is to conduct a study involving human subjects (naïve 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.