Classification & Detection of Banknotes Using Machine Learning

Abstract

Abstract: The only valuable resource in our country is bank currency, which is why money launderers inject fake notes that mimic genuine notes into the financial system to create imbalances. When the market is demonetized, there is a discernible quantity of counterfeit money floating about. In most cases, a human cannot easily tell the difference between a genuine note and a counterfeit one, even when many of the features of a counterfeit note are the same. Automated systems must beaccessible at banks or ATMs in order to identify bank currency authenticity. Since counterfeit banknotes are meticulously crafted, an effective algorithm that can determine whether a note is real or false must be createdin order to create such an automated system. In this study,we use a dataset from the UCI machine learning repository to apply the CNN algorithm. In order to put this into practice, we used machine learning algorithms and evaluated how well they performed using a range of quantitative analytical metrics.