Toward an attack-sensitive tamper-resistant biometric recognition with a symmetric matcher: A fingerprint case study

In order to render a biometric system robust against malicious tampering, it is important to understand the different types of attack and their impact as observed by the liveness and matching scores. In this study, we consider zero-effort impostor attack (referred to as the Z-attack), nonzero-effort impostor attack such as presentation attack or spoofing (S-attack), and other categories of attack involving tampering at the template level (U- and T-attacks). In order to elucidate the impact of all possible attacks, we (1) introduce the concepts of source of origin and symmetric biometric matchers, and (2) subsequently group the attacks into four categories. These views not only improve the understanding of the nature of different attacks but also turn out to ease the design of the classification problem. Following this analysis, we design a novel classification scheme that can take full advantage of the attack-specific data characteristics. Two realisations of the scheme, namely, a mixture of linear classifiers, and a Gaussian Copula-based Bayesian classifier, turn out to outperform a strong baseline classifier based on SVM, as supported by fingerprint spoofing experiments.