Forgery quality and its implications for behavioral biometric security.
Biometric security is a topic of rapidly growing importance in the areas of user authentication and cryptographic key generation. In this paper, we describe our steps toward developing evaluation methodologies for behavioral biometrics that take into account threat models that have been largely ignored. We argue that the pervasive assumption that forgers are minimally motivated (or, even worse, naive) is too optimistic and even dangerous. Taking handwriting as a case in point, we show through a series of experiments that some users are significantly better forgers than others, that such forgers can be trained in a relatively straightforward fashion to pose an even greater threat, that certain users are easy targets for forgers, and that most humans are a relatively poor judge of handwriting authenticity, and hence, their unaided instincts cannot be trusted. Additionally, to overcome current labor-intensive hurdles in performing more accurate assessments of system security, we present a 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.
Ballard
Lucas
L
Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.
Lopresti
Daniel
D
Monrose
Fabian
F
D000465:Algorithms; D001185:Artificial Intelligence; D001699:Biometry / Q000379:methods*; D016494:Computer Security; D001330:Electronic Data Processing / Q000379:methods*; D005607:Fraud; D006236:Handwriting; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D011786:Quality Control