Approach for Detecting Face Morphing Attacks Using Convolution Neural Network

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 .