An Explainable Counterfeit and Genuine Ethiopian Banknote Classification

Counterfeiting is a serious crime with significant impact around the world and in Ethiopia in particular. The National Bank of Ethiopia has implemented various countermeasures to combat counterfeiting. The most successful counterfeit banknote detectors in use today are cash counters, which are hardware-based systems that use optical and magnetic sensors to detect and confirm banknotes. This technology entails an excessive cost and low availability for the public and small businesses, where the largest cash circulation occurs outside of banks. Existing countermeasures are insufficient to address this critical issue. Advancements in technology, such as digital printing and sophisticated scanning equipment, have made it easier for counterfeiters to mislead their victims by producing banknotes nearly identical to genuine ones. Only a handful of studies have been conducted on the current Ethiopian banknotes. This study presents an explainable deep learning-based model for the classification of genuine and counterfeit Ethiopian banknotes. The study used transfer learning with SHAP (Shapley Additive Explanations) and TF-EXPLAIN (TensorFlow Explain) explainable artificial intelligence frameworks for a better understanding of the classification prediction behind the models. Experimental results show that Dense121 achieved the best accuracy of 99.87%, and InceptionV3 achieved a remarkably similar result of 99.50%. To demonstrate the practical application of the model, a mobile application prototype was developed using Flutter and TensorFlow Lite. The application allows users to capture or upload images of banknotes for real-time classification without requiring internet connectivity. This solution provides an accessible and cost-effective counterfeit detection tool for the general public and small businesses.