TY - GEN
T1 - Alpha-Wolves and Alpha-mammals
T2 - 2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2024
AU - Banerjee, Sudipta
AU - Jain, Anubhav
AU - Jiang, Zehua
AU - Memon, Nasir
AU - Togelius, Julian
AU - Ross, Arun
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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 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 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.
AB - 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 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 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.
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U2 - 10.1109/WACVW60836.2024.00117
DO - 10.1109/WACVW60836.2024.00117
M3 - Conference contribution
AN - SCOPUS:85191737444
T3 - Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2024
SP - 1072
EP - 1081
BT - Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2024
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 January 2024 through 8 January 2024
ER -