  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.
