Detection of Fake Currency Using Machine Learning Models

The goal of this research is to determine whether a given cash sample is genuine or counterfeit. Based on the colours, widths, and serial numbers described, several conventional procedures and methods exist for identifying counterfeit cash. Image processing proposes a number of machine learning techniques with a false-identity detection success rate of 99.9 percent for paper cash in today's era of modern computing. In algorithm-based techniques for detection and identification, various entities such as color, form, paper width, and image filtering on the note play a crucial role. This research proposes the application of K-Nearest Neighbors (KNN) followed by image processing as an effective method for spotting counterfeit money. KNN is favored for use in computer vision problems due to its outstanding accuracy, particularly when dealing with small datasets. This approach leverages the strengths of KNN in handling limited data to enhance the precision and reliability of counterfeit money detection. The accurate facts and information on entities and attributes associated to currency have been compiled in this banknote authentication dataset, which was developed using advanced computational and mathematical methodologies. AI calculations and picture handling are utilized for information handling and information extraction to accomplish an elevated degree of exactness and accuracy.