Advanced Image Processing for Authenticity Verification of Banknotes

Abstract

This study delves into the intricate problem of counterfeit currency detection, leveraging the power of advanced image processing and machine learning techniques. Traditionally, identifying counterfeit banknotes relied on characteristics such as color, width, and serial numbers. However, in the era of cutting-edge computer science and high computational capabilities, this research explores a novel approach with machine learning algorithms, achieving a remarkable 99.9% accuracy in counterfeit currency identification. The methodology encompasses a multifaceted analysis, including parameters like color, shape, and paper width, enabled through image filtering. Specifically, this paper advocates the utilization of the K-Nearest Neighbors (KNN) algorithm in conjunction with image processing for counterfeit money identification. KNN, known for its exceptional accuracy, is a promising candidate for computer vision tasks, particularly well-suited for smaller data sets. The creation of a robust dataset for banknote authentication involved intricate computational and mathematical techniques, ensuring the reliability of the data related to currency attributes. Machine learning algorithms and image processing techniques synergize to process data and extract invaluable insights, ultimately delivering the desired level of precision and accuracy.