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Unimodal‐Bio‐GAN: Keyless biometric salting scheme based on generative adversarial network
Author(s) -
Tarek Mayada,
Hamouda Eslam,
ElMetwally Sara
Publication year - 2021
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
eISSN - 2047-4946
pISSN - 2047-4938
DOI - 10.1049/bme2.12034
Subject(s) - biometrics , computer science , key (lock) , salting , authentication (law) , artificial intelligence , transformation (genetics) , pattern recognition (psychology) , computer security , biochemistry , chemistry , food science , gene
Cancellable biometrics enabled us to develop robust authentication systems by replacing the storage of the original biometric template with another secured version. A technique called biometric salting uses a parameter (key) and an invertible function to transform the human biometrics features into a secured format that can be protected and stored securely in a biometric database system. The salting key plays a main role in the success of this transformation, which makes it robust or vulnerable to many security attacks. One of the main challenges that faces biometrics' researchers currently is how to design and protect such a salting key considering two basic measures: security and recognition accuracy. In this article, we propose unimodal‐Bio‐GAN, a reliable keyless biometric salting technique based on standard generative adversarial network (GAN). In unimodal‐Bio‐GAN, a random permuted version of the human biometric data is implicitly considered as a salting key and required only during the enrolment stage, which increases the system reliability to overcome different security attacks. The experimental results of unimodal‐Bio‐GAN using the CASIA Iris‐V3‐Internal database outperform the previous methods and its security efficiency is analysed using different attack types.

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