Open Access

ARTICLE

Deep Learning-Based Robust Morphed Face Authentication Framework for Online Systems

Harsh Mankodiya¹, Priyal Palkhiwala¹, Rajesh Gupta^1,*, Nilesh Kumar Jadav¹, Sudeep Tanwar¹, Osama Alfarraj², Amr Tolba², Maria Simona Raboaca^3,4,*, Verdes Marina⁵

1 Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, 382481, India
2 Computer Science Department, Community College, King Saud University, Riyadh, 11437, Saudi Arabia
3 Doctoral School, University Politehnica of Bucharest, Bucharest, 060042, Romania
4 Department of Hydrogen and Fuel Cell, National Research and Development Institute for Cryogenics and Isotopic Technologies, Ramnicu Valcea, 240050, Romania
5 Department of Building Services, Faculty of Civil Engineering and Building Services, Technical University of Gheorghe Asachi, Iasi, 700050, Romania

* Corresponding Authors: Rajesh Gupta. Email: ; Maria Simona Raboaca. Email:

Computers, Materials & Continua 2023, 77(1), 1123-1142. https://doi.org/10.32604/cmc.2023.038556

Received 18 December 2022; Accepted 20 June 2023; Issue published 31 October 2023

Abstract

The amalgamation of artificial intelligence (AI) with various areas has been in the picture for the past few years. AI has enhanced the functioning of several services, such as accomplishing better budgets, automating multiple tasks, and data-driven decision-making. Conducting hassle-free polling has been one of them. However, at the onset of the coronavirus in 2020, almost all worldly affairs occurred online, and many sectors switched to digital mode. This allows attackers to find security loopholes in digital systems and exploit them for their lucrative business. This paper proposes a three-layered deep learning (DL)-based authentication framework to develop a secure online polling system. It provides a novel way to overcome security breaches during the face identity (ID) recognition and verification process for online polling systems. This verification is done by training a pixel-2-pixel Pix2pix generative adversarial network (GAN) for face image reconstruction to remove facial objects present (if any). Furthermore, image-to-image matching is done by implementing the Siamese network and comparing the result of various metrics executed on feature embeddings to obtain the outcome, thus checking the electorate credentials.

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Keywords

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