IoT-Enabled AI System for Real-Time Fake Currency Detection and Denomination Identification

Abstract

The proposed system proposes a low-cost, sensor-driven embedded system using an Arduino microcontroller to address the rising threat of counterfeit currency. Unlike resource-heavy machine learning approaches, this architecture utilizes light sensors—such as a Light Dependent Resistor (LDR) or BH1750—to analyze the optical characteristics of banknotes. The system works by illuminating a note with a controlled light source and measuring the reflected or transmitted intensity. Because genuine currency possesses unique materials and security features, it produces consistent optical patterns that differ significantly from counterfeits. These readings are processed via a threshold-based algorithm to verify authenticity instantly. Beyond detection, the system features automatic denomination recognition and counting. Since each note value corresponds to a specific light intensity range, the device identifies the denomination and updates a cumulative total. Results, including authenticity status and total value, are displayed on an LCD module. Designed for portability and energy efficiency, this solution is ideal for small businesses, retail shops, and banks. Experimental evaluations show high accuracy and fast response times, effectively reducing human error in financial transactions. While currently optimized for visible light, future iterations may integrate UV or IR sensing to further enhance robustness against sophisticated forgeries. This approach offers a simple, accessible, and effective alternative to complex, expensive detection hardware.