Accuracy Limits as a Barrier to Biometric System Security

Biometric systems are widely used for identity verification and identification, including authentication (i.e., one-to-one matching to verify a claimed identity) and identification (i.e., one-to-many matching to find a subject in a database). The matching process relies on measuring similarities or dissimilarities between a fresh biometric template and enrolled templates. The False Match Rate FMR is a key metric for assessing the accuracy and reliability of such systems. This paper analyzes biometric systems based on their FMR, with two main contributions. First, we explore untargeted attacks, where an adversary aims to impersonate any user within a database. We determine the number of trials required for an attacker to successfully impersonate a user and derive the critical population size (i.e., the maximum number of users in the database) required to maintain a given level of security. Furthermore, we compute the critical FMR value needed to ensure resistance against untargeted attacks as the database size increases. Second, we revisit the biometric birthday problem to evaluate the approximate and exact probabilities that two users in a database collide (i.e., can impersonate each other). Based on this analysis, we derive both the approximate critical population size and the critical FMR value needed to bound the likelihood of such collisions occurring with a given probability. These thresholds offer insights for designing systems that mitigate the risk of impersonation and collisions, particularly in large-scale biometric databases. Our findings indicate that current biometric systems fail to deliver sufficient accuracy to achieve an adequate security level against untargeted attacks, even in small-scale databases. Moreover, state-of-the-art systems face significant challenges in addressing the biometric birthday problem, especially as database sizes grow.

A Comprehensive Analysis on the Leakage of Fuzzy Matchers

This paper provides a comprehensive analysis of information leakage during distance evaluation, with an emphasis on threshold-based obfuscated distance (i.e., Fuzzy Matcher). Leakage can occur due to a malware infection or the use of a weakly privacy-preserving matcher, exemplified by side channel attacks or partially obfuscated designs. We provide an exhaustive catalog of information leakage scenarios as well as their impacts on the security concerning data privacy. Each of the scenarios leads to generic attacks whose impacts are expressed in terms of computational costs, hence allowing the establishment of upper bounds on the security level.

Untargeted Near-collision Attacks in Biometric Recognition

A biometric recognition system can operate in two distinct modes, identification or verification. In the first mode, the system recognizes an individual by searching the enrolled templates of all the users for a match. In the second mode, the system validates a claimed identity by comparing the fresh template with the enrolled template for this identity. Both the experimentally determined false match rate and false non-match rate through recognition threshold adjustment define the recognition accuracy, and hence the security of the system. The biometric transformation schemes usually produce binary templates that are better handled by cryptographic schemes. One of the requirements for these transformation schemes is their irreversibility. In this work, we rely on probabilistic modelling to quantify the security strength of binary templates. We investigate the influence of template size, database size and threshold on the probability of having a near-collision, and we highlight two attacks on biometric systems. We discuss the choice of parameters through the generic presented attacks.

Authentication Attacks on Projection-based Cancelable Biometric Schemes

Cancelable biometric schemes aim at generating secure biometric templates by combining user specific tokens, such as password, stored secret or salt, along with biometric data. This type of transformation is constructed as a composition of a biometric transformation with a feature extraction algorithm. The security requirements of cancelable biometric schemes concern the irreversibility, unlinkability and revocability of templates, without losing in accuracy of comparison. While several schemes were recently attacked regarding these requirements, full reversibility of such a composition in order to produce colliding biometric characteristics, and specifically presentation attacks, were never demonstrated to the best of our knowledge. In this paper, we formalize these attacks for a traditional cancelable scheme with the help of integer linear programming (ILP) and quadratically constrained quadratic programming (QCQP). Solving these optimization problems allows an adversary to slightly alter its fingerprint image in order to impersonate any individual. Moreover, in an even more severe scenario, it is possible to simultaneously impersonate several individuals.