Advanced Methods for Identifying Counterfeit Currency: Using Deep Learning and Machine Learning

Abstract

Counterfeiting is a serious threat to economies because sophisticated counterfeit banknotes are becoming increasingly difficult to identify through conventional verification techniques, thanks to advancements in printing technology. In this work, we offer a thorough investigation of sophisticated methods for detecting counterfeit money that make use of deep learning and machine learning approaches. Using machine learning algorithms like Random Forest, Decision Tree Classifier, XGBoost, CatBoost, and Support Vector Machine (SVM) in addition to deep learning techniques like Convolutional Neural Networks (CNNs), VGG16, MobileNetV2, and InceptionV3, we examine the security characteristics of Iraqi dinar banknotes and build robust models. All of the models in our results had high accuracy rates, with CNN, CatBoost, and SVM showing particularly strong performance. These results demonstrate how effective cutting-edge technical solutions are in thwarting the dangers posed by counterfeit money, protecting national economies and reducing losses. Sustaining the security of international financial systems and keeping ahead of evolving counterfeiting strategies need ongoing study and development in this area.