Exploring the Potential of Secure Signature Verification by employing a Trio Integrated Approach using GAN, Kernelized Biohashing and BiLSTM

Abstract

In the realm of digital authentication, the security and accuracy of signature verification systems are paramount. This paper proposes a new approach for signature verification that is able to greatly improve the security and reliability through the synergy given by Generative Adversarial Networks (GANs), Kernelized Biohashing, and Bidirectional Long Short-Term Memory (BiLSTM) networks. The system proposed in this paper is based on cancellable biometrics which is a novel method that converts biometric data into new, secure, revocable domains, while allowing the user's privacy to be protected from any kind of misuse of data. First, we used GANs to generate a large and diverse synthetic signature dataset. This dataset is not only rich in quantity and diversity but also closely modeled on the genuine signatures' complex patterns and variations. The synthetic signatures go through Kernelized Biohashing, successfully converting them into a secure and encrypted form, preserving privacy while being able to capture essential integrity in signature biometric verification processes. The biohaseddata isanalyzed through the verification process using the BiLSTM networks. BiLSTM networks allow analysis of temporal dynamics with other attributes of each signature, in processing sequences and obtaining long-term dependencies. Our system applies these networks in bio-hashed data and consequently attains good accuracy in the verification of signature, thus distinguishing well between a genuine and a forged signature. In this integrated framework, the identified mechanisms assure the security and reliability of the digital authentications not only for the acute security and reliability challenges being faced by the traditional signature verification systems—such as forgery, data breach, and others—but they also set a new benchmark altogether. Our approach takes a significant leap forward in protecting user signatures while ensuring, through the application of cancelable biometrics and advanced machine learning techniques, the integrity of verification processes.