An Advanced Image Processing System for Counterfeit Currency Detection: Architecture, Methodology, And Feature Analysis

Abstract

The proliferation of counterfeit currency poses a serious threat to global and national economies, disproportionately affecting individuals who lack access to sophisticated verification tools. This report introduces an innovative, image processing-based system developed in Python to bridge this accessibility gap by enabling average users to authenticate currency notes with ease. The system follows a structured sequence—image acquisition, grayscale conversion, edge detection, segmentation, feature extraction, and comparison—to accurately distinguish between genuine and counterfeit notes. Unlike traditional binary verification tools, this system enhances transparency and trust by visually highlighting discrepancies on suspected counterfeits. Designed for user-friendliness and potential mobile integration, the solution democratizes financial security, addressing a socio-economic imbalance by empowering individuals with the tools to protect themselves. Its adaptable framework also allows for future expansion to include multiple currencies, marking a significant step toward global financial protection and reinforcing public confidence in the integrity of currency systems.