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

作　　者：Harsh Mankodiya Priyal Palkhiwala Rajesh Gupta Nilesh Kumar Jadav Sudeep Tanwar Osama Alfarraj Amr Tolba Maria Simona Raboaca Verdes Marina 

机构地区：[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

出　　处：《Computers, Materials & Continua》2023年第10期1123-1142,共20页计算机、材料和连续体（英文）

基　　金：funded by the Researchers Supporting Project Number(RSP2023R 102)King Saud University,Riyadh,Saudi Arabia.

摘　　要：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.

关 键 词：Artificial intelligence DISCRIMINATOR GENERATOR Pix2pix GANs Kullback-Leibler(KL)-divergence online voting system Siamese network 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]