Abstract: This report covers the development and implementation of a Fake Currency Detection System that uses machine learning and image processing. The rise of counterfeit banknotes presents a serious threat to financial systems and public trust. This project tackles this issue by creating an automated software solution that accurately distinguishes genuine currency from counterfeit notes. The system uses Python, OpenCV, and Scikit-learn to process images of currency notes, extract meaningful features, and classify them with a Support Vector Machine (SVM) algorithm. A user-friendly Tkinter-based Graphical User Interface (GUI) allows users to upload an image and receive an instant prediction of authenticity. The project showcases how AI can improve financial security while providing a cost-effective and scalable alternative to traditional hardware detectors.

Keywords: Fake currency detection, machine learning, image processing, support vector machine, Tkinter GUI.

Downloads: | DOI: 10.17148/IARJSET.2026.13460