Fake Currency Detection with Machine Learning Algorithm and Image Processing

Abstract

The issue concerning whether a sample of money is fake can be found in this study. Older methods of detecting the fake money include the investigation of such features as colors, width, and serial numbers. During this modern age of computer science and higherlevel computing, picture processing has been applied to develop several ML algorithms that can detect fake money with 99.9 percent accuracy. These finding and identification methods involve the utilization of such things as color, shape, paper thickness, and picture filtering. In this case, KNN and image processing are applied to propose a means of detecting fake money. KNN is effective when dealing with small data and hence suitable when used in computer vision. This method generates a banknote authentication data set using the sophisticated computer and mathematical techniques, which have accurate data regarding the properties and attributes of the money. The final results are obtained with the ML algorithms and image processing to ensure that they are accurate in the data processing and extraction.