Fake-Indian-Currency-Detection with Deep Learning Based-Xception CNN
B. M. Chaitra · International Journal for Research in Applied Science and Engineering Technology · 2025
Counterfeit money is still a major problem for banks and the economy. With the growing complexity of counterfeiters, conventional ways of identifying fake money are less effective. The current research introduces a deep learning-based approach for identifying fake Indian currency notes of ₹100, ₹200, and ₹500 denominations. With the help of the Xception model, which is good at detecting minute image details, the system becomes adept at distinguishing subtle differences between real and fake notes. The model is trained on a data set with images of genuine and counterfeit notes, enabling it to learn intricate patterns and subtle differences that tend to be difficult to spot using the naked eye. Experiments show that the Xception-based model achieve superior classification accuracy and offers an efficient productive solution for high-speed, accurate, and automated counterfeiting detection in real-world scenarios. A range of data such as images of genuine and fake banknotes under various conditions are used in an attempt to enhance the robustness of the model. The proposed system demonstrates high accuracy and reliability, indicating that it can be applied to real-time counterfeit detection systems for enhanced security and confidence in financial transactions.