Fake Currency Detection using Machine Learning and Computer Vision

he Circulation of counterfeit money is a critical economic and social problem worldwide, calling for efficient detection systems. Conventional manual processes and UV scanning are time-consuming and prone to errors. This paper suggests an automated counterfeit currency detector system based on machine learning and computer vision. The system utilizes high-level image processing algorithms such as ORB (Oriented FAST and Rotated BRIEF) and SSIM (Structural Similarity Index Measure) for feature extraction and matching, and proprietary algorithms for unique security features such as bleed lines and number panels. Dependent on Python, OpenCV, and TensorFlow implementation, the system has high accuracy in detection of up to 83% against counterfeit notes and 80% against authentic notes and processes relatively fast with the processing time taken being around 5 seconds for each note. The framework poses promising applications for banking, retail, and police forces, toward financial security as well as consumer confidence.