Approach for Detecting Face Morphing Attacks Using Convolution Neural Network

Authors

Essa M. Namis, College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq.Follow
Khalid Shaker, College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq.
Sufyan Al-Janabi, College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq.

Document Type

Article

Keywords

Face Recognition Systems (FRSs), Convolution Neural Network (CNN), Morphing attacks, Deep learning, Face detection

Abstract

The facial morphing method combines at least two images of the face to get a singular altered facial image that exposes the vulnerabilities of face recognition systems (FRS). The extensive implementation of face recognition algorithms, particularly in Automatic Border Control (ABC) systems, has raised apprehensions over potential threats, as modified passports present significant risks to national security. In this paper, a new face morphing attack detection approach has been proposed using two different datasets (StyleGAN and AMSL) for testing and validation. A new model for face morphing attack detection based on a special Convolutional Neural Networks (CNNs) architecture has been used for classifying real and morphing images. Experimental results indicate that our suggested approach is extremely generalized and markedly robust in detecting face morphing attacks produced by different techniques .

How to Cite This Article

Namis, Essa M.; Shaker, Khalid; and Al-Janabi, Sufyan (2025) "Approach for Detecting Face Morphing Attacks Using Convolution Neural Network," Mesopotamian Journal of Computer Science: Vol. 5: Iss. 1, Article 5.
DOI: https://doi.org/10.58496/MJCSC/2025/005
Available at: https://map.researchcommons.org/mjcsc/vol5/iss1/5