Mathematical Foundations of Machine Learning for Detecting Counterfeit Currency

Abstract

Due to advancements in printing technology and digital scanning tools, eliminating counterfeit bills has become increasingly challenging. As a powerful solution, machine learning has provided an efficient and automatic manner to facilitate fake bill detection. Here, we focus on the mathematical foundations of machine learning, specifically supervised learning, to develop a neural network capable of detecting fake bills. Using a dataset of dollar bills, key statistic features of images of the bills, e.g., variance and skewness, are analyzed. We unveil how these features are processed through a neural network that leverages layers, nodes, activation functions, and binary cross-entropy errors for accuracy in detection. As a practical example, we create and train a neural network model that performs well in counterfeit detection with a superior high accuracy. By reinforcing core mathematical concepts essential to building such systems, we demonstrate the flexibility and power of machine learning techniques, which may promise the same level of excellence in other applications, such as credit card fraud prevention, identity theft detection, and anomaly detection in financial transactions.