Neural Currency Guard using Generative Adversarial Networks

Abstract

Counterfeit currency detection remains an ongoing challenge worldwide, as counterfeiters continuously enhance their techniques. This paper provides a comprehensive review of recent advancements in counterfeit currency detection systems, with a specific focus on innovative approaches using Generative Adversarial Networks (GANs). We implement GANs to generate realistic synthetic currency images for training robust counterfeit detectors. By thoroughly analyzing current research, we gain valuable perspectives into GAN-based methodologies for producing diversified fake currency data. Our study also examines the utilization of image processing, machine learning, and spectroscopic techniques in existing counterfeit recognition systems. Additionally, we detail a practical implementation of GANs for generating counterfeit currency images in Indian context, as well as evaluate its effectiveness. This research aims to deliver vital insights into cutting-edge counterfeit currency detection, presenting fresh perspectives on harnessing GANs. It also intends to aid future research by highlighting potential areas for improvement.