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Title: Alpha-Wolves and Alpha-mammals: Exploring Dictionary Attacks on Iris Recognition Systems
A dictionary attack in a biometric system entails the use of a small number of strategically generated images or tem- plates to successfully match with a large number of identi- ties, 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 bit- wise 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 vulnerabil- ity 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.  more » « less
Award ID(s):
1956200
PAR ID:
10559757
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
IEEE
Date Published:
ISBN:
979-8-3503-7028-7
Page Range / eLocation ID:
1072 to 1081
Format(s):
Medium: X
Location:
Waikoloa, HI, USA
Sponsoring Org:
National Science Foundation
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