Face Morphing Attack Detection-A Literature Review

Abstract

Face morphing is a sophisticated kind of image manipulation that involves combining two or more facial photographs to create a new identity. This technique is becoming more and more common in digital media, and it might lead to major issues including fraud, identity theft, and the propagation of bogus identities. The goal of this study is to solve these problems by developing a face-morphing detection system that uses state-of-the-art computer vision and machine learning techniques to be both accurate and efficient. To identify morphed faces, the proposed method examines minuscule aberrations and inconsistencies created during the morphing process. It accomplishes this by applying feature extraction methods and deep neural networks. By evaluating the system's resilience on a large dataset that encompasses several morphing techniques and lighting configurations, its effectiveness is confirmed in a multitude of scenarios.