Assessing Machine Learning Algorithms for Real-Time Fake Currency Detection

Counterfeiting poses a significant threat as the circulation of fake currency diminishes the value of genuine notes, thereby disrupting the country's economic stability. Addressing this issue is critical before the proliferation of counterfeit notes becomes unmanageable. Manual detection methods are often unreliable since counterfeit notes are produced with materials and inks that closely resemble the original. Across the country, counterfeit detection is typically carried out using hardware-based systems. However, these methods are time-consuming and struggle to process large volumes efficiently. To address these challenges and streamline the process of counterfeit currency detection, this study proposes an image processing-based computational technique. The objective is to accurately determine whether a given note is genuine or counterfeit, with a high prediction rate. This detection is enhanced using deep learning algorithms, which analyze key attributes such as color, form, paper thickness, serial numbers, and image filters on the currency. The proposed model is trained on a real-time dataset consisting of both genuine and counterfeit notes. Experimental results demonstrate that this method achieves an overall accuracy of 96.41%.