Detecting Fake Currency: A Comparative Study of Feature-Based and Image-Based Analysis

Abstract: Counterfeit money is a huge threat for the economy and society. Traditional ways of detecting fake notes works well in controlled environments, but they are not fast enough, cannot handle a lot of cases and are not always easy to use in real life. The rise of Artificial Intelligence (AI) offers powerful tools for automatic and better fraud detecting solutions. It helps in closing gap between simple checks and real-time verification. This study compares two AI methods for finding counterfeit currency. One method uses statistical features from banknotes and processes them with Logistic Regression and K-Nearest Neighbours (KNN). The other method uses images and OpenCV to check visual security features like watermarks and security strips. The results show that the feature-based method is better at accuracy and speed for structured data, while the image-based approach works well for real world situations like mobile-verification. The study also applies these methods to prevent fraud in electronic payment systems like UPI and mobile banking, showing how AI can protect both physical and digital transactions.

Keywords: Anti-Counterfeiting Currency Detection, Financial Fraud Prevention, Machine Learning, Image Processing, Logistic Regression, K-Nearest Neighbours, OpenCV, Digital Payment Security, UPI Fraud Detection, Artificial Intelligence in Finance.