Applications of Machine Learning in Detecting Afghan Fake Banknotes

TL;DR

This paper presents a machine learning-based image processing method to detect counterfeit Afghan banknotes, achieving 99% accuracy, addressing the lack of accessible authentication tools for the public.

Contribution

The study introduces a novel approach combining image processing and machine learning to accurately identify fake Afghan banknotes using publicly accessible methods.

Findings

Random Forest achieved 99% accuracy

Effective detection of counterfeit banknotes

Utilized statistical features and machine learning models

Abstract

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 Na\"ive 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.