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Creators/Authors contains: "Banerjee, Sudipta"

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  1. ; (, IEEE Transactions on Biometrics, Behavior, and Identity Science)
    Free, publicly-accessible full text available January 1, 2026
  2. ; ; ; ; ; (, IEEE)
    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. 
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  3. ; ; ; ; ; (, IEEE Transactions on Biometrics, Behavior, and Identity Science)
    Full Text Available 
  4. ; ; ; ; (, International Joint Conference on Biometrics)
    Full Text Available 
  5. ; ; (, 2022 IEEE International Joint Conference on Biometrics (IJCB))
    Full Text Available 
  6. ; ; (, Computer Vision and Image Understanding)
    Full Text Available 
  7. ; (, 2021 IEEE International Joint Conference on Biometrics (IJCB))
    Full Text Available 
  8. ; (, IEEE Transactions on Biometrics, Behavior, and Identity Science)
    Full Text Available