  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.
