Biometric Authentication Revisited: Understanding the Impact of Wolves in Sheep's Clothing

Lucas Ballard, Fabian Monrose, and Daniel Lopresti

Abstract

Biometric security is a topic of rapidly growing importance, especially as it applies to user authentication and key generation. In this paper, we describe our initial steps towards developing evaluation methodologies for behavioral biometrics that take into account threat models which have largely been ignored. We argue that the pervasive assumption that forgers are minimally motivated (or, even worse, naive), or that attacks can only be mounted through manual effort, is too optimistic and even dangerous. To illustrate our point, we analyze a handwriting-based key-generation system and show that the standard approach of evaluation significantly over-estimates its security. Additionally, to overcome current labor-intensive hurdles in performing more accurate assessments of system security, we present a {\em generative attack} model based on concatenative synthesis that can provide a rapid indication of the security afforded by the system. We show that our generative attacks match or exceed the effectiveness of forgeries rendered by the skilled humans we have encountered.

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