Smart Detection of Indian Counterfeit Currency Notes using Deep Learning Techniques

This chapter delves into the critical issue of counterfeit currency detection, emphasizing the limitations of traditional methods and the advantages of deep learning techniques. The study focuses on creating a custom dataset of Indian banknotes, including various denominations and both real and counterfeit samples, to train and validate deep learning models. Two prominent CNN architectures, AlexNet and InceptionV3, are employed using transfer learning to achieve high accuracy in distinguishing genuine from counterfeit notes. The research addresses key challenges such as dataset imbalance and noise sensitivity through aggressive data augmentation techniques. The results demonstrate that InceptionV3 outperforms AlexNet, achieving exceptional precision, recall, and F1-scores. Furthermore, the study integrates the trained models into a user-friendly Flask-based web application, enabling real-time detection via image upload or webcam capture. This practical deployment bridges the gap between research and real-world application, making the solution robust, scalable, and accessible to financial, commercial, and public sectors. The chapter concludes by highlighting the potential for future enhancements, such as multi-currency detection and blockchain-based transaction recording, to further secure the financial ecosystem.