Spoofing Face Detection Using Novel Edge-Net Autoencoder for Security

Title

Spoofing Face Detection Using Novel Edge-Net Autoencoder for Security

Creator

Alharbi A.H.; Karthick S.; Venkatachalam K.; Abouhawwash M.; Khafaga D.S.

Description

Recent security applications in mobile technologies and computer systems use face recognition for high-end security. Despite numerous security tech-niques, face recognition is considered a high-security control. Developers fuse and carry out face identification as an access authority into these applications. Still, face identification authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized user. In the existing spoofing detection algorithm, there was some loss in the recreation of images. This research proposes an unobtrusive technique to detect face spoofing attacks that apply a single frame of the sequenced set of frames to overcome the above-said problems. This research offers a novel Edge-Net autoencoder to select convoluted and dominant features of the input diffused structure. First, this pro-posedmethodistestedwiththeCross-ethnicityFaceAnti-spoofing (CASIA), Fetal alcohol spectrum disorders (FASD) dataset. This database has three models of attacks: distorted photographs in printed form, photographs with removed eyes portion, and video attacks. The images are taken with three different quality cameras: low, average, and high-quality real and spoofed images. An extensive experimental study was performed with CASIA-FASD, 3 Diagnostic Machine Aid-Digital (DMAD) dataset that proved higher results when compared to existing algorithms. 2023, Tech Science Press. All rights reserved.

Source

Intelligent Automation and Soft Computing, Vol-35, No. 3, pp. 2773-2787.

Date

2023-01-01

Publisher

Tech Science Press

Subject

auto-encoder; digital security; edge detection; edge net; face authentication; Image processing

Coverage

Alharbi A.H., Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia; Karthick S., Department of Electrical Electronics and Communication Engineering, GITAM School of Technology, GITAM Deemed to be University, Bengaluru Campus, Karnataka, 560065, India; Venkatachalam K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Abouhawwash M., Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt, Department of Computational Mathematics, Science, and Engineering (CMSE), Michigan State University, East Lansing, 48824, MI, United States; Khafaga D.S., Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia

Rights

All Open Access; Hybrid Gold Open Access

Relation

ISSN: 10798587

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.32604/iasc.2023.030763

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137350144&doi=10.32604%2fiasc.2023.030763&partnerID=40&md5=a2d99d8905f8911367cd4774450a6eb4