Analysis of Master Vein Attacks on Finger Vein Recognition Systems
Huy H. Nguyen, Trung-Nghia Le, Junichi Yamagishi, and Isao Echizen

TL;DR
This paper introduces novel master vein attack methods on finger vein recognition systems, demonstrating high false acceptance rates and raising concerns about their security robustness.
Contribution
It is the first to propose master vein attacks using generative models and adversarial machine learning, significantly advancing attack techniques on FVR systems.
Findings
Achieved false acceptance rates up to 88.79% with combined methods.
Demonstrated vulnerability of FVR systems to non-vein-looking samples.
Highlighted the need for improved security measures against master vein attacks.
Abstract
Finger vein recognition (FVR) systems have been commercially used, especially in ATMs, for customer verification. Thus, it is essential to measure their robustness against various attack methods, especially when a hand-crafted FVR system is used without any countermeasure methods. In this paper, we are the first in the literature to introduce master vein attacks in which we craft a vein-looking image so that it can falsely match with as many identities as possible by the FVR systems. We present two methods for generating master veins for use in attacking these systems. The first uses an adaptation of the latent variable evolution algorithm with a proposed generative model (a multi-stage combination of beta-VAE and WGAN-GP models). The second uses an adversarial machine learning attack method to attack a strong surrogate CNN-based recognition system. The two methods can be easily…
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Videos
Analysis of Master Vein Attacks on Finger Vein Recognition Systems· youtube
Taxonomy
TopicsBiometric Identification and Security · Forensic Fingerprint Detection Methods · Forensic and Genetic Research
MethodsBeta-VAE
