Fake Currency Detection Using Image Processing System

Fake currency detection is a crucial task in financial security, preventing fraud and ensuring the authenticity of banknotes. This project utilizes image processing techniques to detect counterfeit currency by analyzing various security features such as watermarks, color patterns, texture, and unique identification marks. Using techniques like edge detection, feature extraction, and machine learning, the system can efficiently distinguish between genuine and fake currency. This automated approach enhances accuracy, reduces human error, and provides a reliable solution for counterfeit detection in banking and financial sectors.