Applications of Machine Learning in Detecting Afghan Fake Banknotes
This addresses the issue of fake currency in Afghanistan, which harms the economy, by providing a potential accessible detection tool for the public, though it is an incremental application of existing methods.
The paper tackled the problem of detecting counterfeit Afghan banknotes by developing an image processing and machine learning method, achieving 99% accuracy with the Random Forest algorithm.
Fake currency, unauthorized imitation money lacking government approval, constitutes a form of fraud. Particularly in Afghanistan, the prevalence of fake currency poses significant challenges and detrimentally impacts the economy. While banks and commercial establishments employ authentication machines, the public lacks access to such systems, necessitating a program that can detect counterfeit banknotes accessible to all. This paper introduces a method using image processing to identify counterfeit Afghan banknotes by analyzing specific security features. Extracting first and second order statistical features from input images, the WEKA machine learning tool was employed to construct models and perform classification with Random Forest, PART, and Naïve Bayes algorithms. The Random Forest algorithm achieved exceptional accuracy of 99% in detecting fake Afghan banknotes, indicating the efficacy of the proposed method as a solution for identifying counterfeit currency.