Theoretical vulnerability in likelihood-ratio-based biometric verification

Impersonation by impostors is one of the representative security issues on biometric authentication system. A wolf attack is an attack on biometrics system using a wolf that can be falsely accepted as a match with multiple templates. False acceptance rate (FAR) which has been a conventional standard measure to quantify the average error rates of detecting the impersonation has not taken into consideration that impostors could use artefacts instead of templates generated from an individual. The wolf attack probability (WAP) is thus used as a new measure for evaluating the security of biometric authentication. In this paper, we focus on the vulnerability of likelihood-ratio-based biometric verification scheme that is known as optimal similarity measure in terms of average error rates. First, we present theoretical analysis of a likelihood-ratio-based biometric verification system and show the existence of wolf features under the assumption that there is an approximation error between background model and true feature distribution. Second, we propose a new wolf attack scheme that can achieve 60% of WAP. Furthermore, we empirically evaluate the proposed wolf attack using real biometric data from ATR speech database.