Alpha-wolves and Alpha-mammals: Exploring Dictionary Attacks on Iris Recognition Systems
Abstract
A dictionary attack in a biometric system entails the use of a small number of strategically generated images or templates to successfully match with a large number of identities, thereby compromising security. We focus on dictionary attacks at the template level, specifically the IrisCodes used in iris recognition systems. We present an hitherto unknown vulnerability wherein we mix IrisCodes using simple bitwise operators to generate alpha-mixtures - alpha-wolves (combining a set of "wolf" samples) and alpha-mammals (combining a set of users selected via search optimization) that increase false matches. We evaluate this vulnerability using the IITD, CASIA-IrisV4-Thousand and Synthetic datasets, and observe that an alpha-wolf (from two wolves) can match upto 71 identities @FMR=0.001%, while an alpha-mammal (from two identities) can match upto 133 other identities @FMR=0.01% on the IITD dataset.
Cite
@article{arxiv.2403.12047,
title = {Alpha-wolves and Alpha-mammals: Exploring Dictionary Attacks on Iris Recognition Systems},
author = {Sudipta Banerjee and Anubhav Jain and Zehua Jiang and Nasir Memon and Julian Togelius and Arun Ross},
journal= {arXiv preprint arXiv:2403.12047},
year = {2024}
}
Comments
8 pages, 5 figures, 13 tables, Workshop on Manipulation, Adversarial, and Presentation Attacks in Biometrics, Winter Conference on Applications of Computer Vision