FAKE CURRENCY DETECTION USING MACHINE LEARNING

Counterfeit currency circulation is a growing challenge that threatens financial security, destabilizes economies, and erodes public trust in monetary systems. Traditional detection methods such as watermark inspection, ultraviolet scanning, and manual verification are either time- consuming, costly, or prone to human error. With the increasing sophistication of counterfeit printing technologies, there is a strong need for automated, accurate, and scalable solutions. This research introduces a Fake Currency Detection System based on Machine Learning that leverages image processing and classification algorithms to identify counterfeit banknotes with high accuracy. The proposed system captures images of currency notes and processes them through a combination of feature extraction techniques (including texture, edges, and security markers) and machine learning classifiers such as Convolutional Neural Networks (CNNs). A structured dataset of genuine and fake currency images was used to train and validate the model. Experimental results demonstrate promising detection performance, with an accuracy exceeding 90% under standard testing conditions. Additionally, the system includes a user- friendly interface for real-time detection, making it suitable for banks, retail businesses, and ATMs. This study contributes to the growing field of financial fraud prevention by providing a low-cost, scalable, and intelligent approach to counterfeit detection. Unlike conventional methods, the machine learning-based framework adapts to new counterfeit patterns and improves accuracy as more data becomes available. The findings confirm that the proposed system can serve as a reliable tool in ensuring secure financial transactions and supporting national efforts against currency fraud.