Abstract
<jats:p>This paper explores the application of machine learning (ML) in detecting counterfeit Indian currencyusing the Xception deep learning model, which analyzes images to distinguish genuine notes fromfake ones. Out of the entire dataset, 70% fuelled the training phase, 20% guided validation, and thefinal 10% was reserved for testing to evaluate the model's performance comprehensively. Performanceassessment relied on indicators like correctness and specificity to measure how well the model did.The Xception model achieved an impressive 99% accuracy during training but showed limitations invalidation, performing at only 10%. These challenges highlight issues like dataset imbalance and theneed for effective feature extraction to enhance reliability. The findings underline the potential of MLin aiding banks and law enforcement agencies in identifying counterfeit currency efficiently. However,the study also identifies key areas for improvement, including addressing data imbalances and refiningthe model to improve validation effectiveness. Next-stage exploration will focus on enhancing themodel's robustness, incorporating additional features, and transitioning the model toward real-worldapplications.This research demonstrates the promise of ML in tackling the growing problem of counterfeit currencyand sets a foundation for further advancements in this domain.</jats:p>