In spite of many advantages, multi-modal biometric recognition systems are vulnerable to spoof attacks, which can decrease their level of security. Thus, it is fundamental to understand and analyse the effects of spoof attacks and propose new methods to design robust systems against them. To this aim, we are developing a method based on simulating the fake score distributions of individual matchers, to evaluate the relative robustness of different score fusion rules. We model the score distribution of fake traits by assuming it lies between the one of genuine and impostor scores, and parametrize it by a measure of the relative distance to the latter, named attack strength. Different values of the attack strength account for the many different factors which can affect the distribution of fake scores. In this paper we present preliminary results aimed at evaluating the capability of our model to approximate realistic fake score distributions. To this aim we use a data set made up of faces and fingerprints, including realistic spoof attacks traits.

Robustness Evaluation of Biometric Systems under Spoof Attacks

ROLI, FABIO
2011-01-01

Abstract

In spite of many advantages, multi-modal biometric recognition systems are vulnerable to spoof attacks, which can decrease their level of security. Thus, it is fundamental to understand and analyse the effects of spoof attacks and propose new methods to design robust systems against them. To this aim, we are developing a method based on simulating the fake score distributions of individual matchers, to evaluate the relative robustness of different score fusion rules. We model the score distribution of fake traits by assuming it lies between the one of genuine and impostor scores, and parametrize it by a measure of the relative distance to the latter, named attack strength. Different values of the attack strength account for the many different factors which can affect the distribution of fake scores. In this paper we present preliminary results aimed at evaluating the capability of our model to approximate realistic fake score distributions. To this aim we use a data set made up of faces and fingerprints, including realistic spoof attacks traits.
2011
978-3-642-24087-4
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1090925
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