Generative adversary neural networks (GANs) in the generation and detection of false faces for biometric security applications

Abstract

This project aims to address the growing issue of identity fraud in digital environments through the research and development of a system based on generative adversarial neural networks for creating and detecting fake faces. Currently, identity fraud poses a significant threat to biometric security in digital settings. Advances in generating fake images, particularly faces, have made secure and reliable authentication increasingly challenging. To tackle this challenge, Generative Adversarial Networks (GANs), a technique used in generating realistic images, will be employed. Research will focus on designing and developing an optimized GAN model for creating and detecting fake faces. The proposed solution involves implementing a comprehensive system that encompasses both the creation and detection of fake faces using artificial intelligence. Machine learning methods and image processing will be employed to develop a robust detection system. Subsequently, testing will be conducted to evaluate the system's performance. Analysis of the results will identify strengths, limitations, and potential areas for improvement, thereby providing recommendations for the effective implementation of the system in biometric security applications.