Indian fake currency detection using image processing and machine learning

Abstract

The escalating production of counterfeit notes, facilitated by advancements in color printing and scanning, poses a significant global challenge impacting economies and security. This issue, prevalent in countries like India, has negative ramifications, including the funding of illegal activities and terrorism. Despite efforts, such as demonetization in 2016, counterfeits persist, necessitating innovative solutions. The proposed model introduces a fake note detection system utilizing computer vision and machine learning, specifically a Convolutional Neural Network (CNN). CNN effectively extracts intricate features from input data, showcasing its proficiency in pattern recognition. Notably, the system focuses on individual security features within banknotes, distinguishing it from other approaches that analyze entire note images. The primary goal is swift and accurate detection and reduction of counterfeit circulation, contributing to the overall security of the economy. The proposed model resulted in an impressive accuracy of 91.66% for all the six security features in the Indian denomination of Rs. 500, 95.25% for all the six security features in the Indian denomination of Rs. 200, 92.66% for all the six security features in the Indian denomination of Rs.100.