Fake Currency Detection

The proliferation of counterfeit currency poses a significant threat to global economies and financial stability. Traditional manual inspection methods are prone to human error, time-consuming, and require specialized knowledge. This paper proposes an automated, real-time counterfeit currency detection system utilizing image processing techniques combined with a supervised machine learning classifier. The proposed methodology leverages key security features, including watermarks, security threads, and intaglio printing patterns, by analyzing high-resolution digital images captured under visible and ultraviolet light. Feature extraction focuses on texture analysis (using Local Binary Patterns), dimensional accuracy, and color spectrum profiling. A Support Vector Machine (SVM) is trained on a robust dataset of genuine and counterfeit banknotes to classify the currency as authentic or fake with high accuracy. The experimental results demonstrate the system's effectiveness and its potential for deployment in automated teller machines (ATMs) and point-of-sale (POS) systems, providing a rapid and reliable solution to combat currency fraud.