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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. [ABSTRACT FROM AUTHOR]

Several cancellable biometrics (CBs) techniques have been proposed to protect biometric data and maintain users' privacy. Although such techniques can withstand brute‐force and/or pre‐image attacks, they are vulnerable to correlation attacks. In this study, the authors propose a novel correlation attack‐resistant CBs scheme that is based on a convolution operation and a bidirectional associative memory (BAM) neural network. The proposed scheme utilises BAM to bind biometric templates to random bit‐strings in a secure and efficient manner. These random bit‐strings are then employed to derive cancellable templates from the true templates linked to them via BAM weights, which are safely stored with the generated cancellable template in the system database. In this study, linear convolution is adopted as the cancellable transformation process. The result of convolving the original biometric template with the transformation key is binarised according to a predefined threshold to thwart blind de‐convolution. The security of the proposed scheme against different attacks is analysed and experiments on the CASIA‐IrisV3‐Interval dataset illustrate the efficacy of the proposed scheme. [ABSTRACT FROM AUTHOR]