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

Harsh Mankodiya1, Priyal Palkhiwala1, Rajesh Gupta1,*, Nilesh Kumar Jadav1, Sudeep Tanwar1, Osama Alfarraj2, Amr Tolba2, Maria Simona Raboaca3,4,*, 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: email; Maria Simona Raboaca. Email: email

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

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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APA Style
Mankodiya, H., Palkhiwala, P., Gupta, R., Jadav, N.K., Tanwar, S. et al. (2023). Deep learning-based robust morphed face authentication framework for online systems. Computers, Materials & Continua, 77(1), 1123-1142. https://doi.org/10.32604/cmc.2023.038556
Vancouver Style
Mankodiya H, Palkhiwala P, Gupta R, Jadav NK, Tanwar S, Alfarraj O, et al. Deep learning-based robust morphed face authentication framework for online systems. Comput Mater Contin. 2023;77(1):1123-1142 https://doi.org/10.32604/cmc.2023.038556
IEEE Style
H. Mankodiya et al., "Deep Learning-Based Robust Morphed Face Authentication Framework for Online Systems," Comput. Mater. Contin., vol. 77, no. 1, pp. 1123-1142. 2023. https://doi.org/10.32604/cmc.2023.038556



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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