Revolutionizing Convolutional Neural Networks for Enhanced Currency Security and Fraud Prevention

Abstract

Traditional methods for fake currency detection rely on attributes such as colours, width, and serial numbers. However, in the era of advanced computer science and computational methods, leveraging machine learning algorithms through image processing has shown remarkable success, achieving the best accuracy rates. This research introduces a novel approach to fake currency recognition using the Convolutional Neural Network (CNN) algorithm combined with image processing. The CNN model is designed to automatically learn and extract features from input images. In the context of fake currency recognition, the CNN algorithm typically consists of multiple layers including convolutional layers responsible for detecting patterns and features, followed by pooling layers for dimensionality reduction. The proposed method involves the implementation of machine learning algorithms and image processing techniques for data processing and extraction. By combining these technologies, the system aims to achieve robust accuracy in identifying counterfeit currency, utilizing features such as colour, shape, paper width, and image filtering on the banknote. The authentication dataset is meticulously crafted to enhance computational and mathematical strategies, ultimately contributing to improved accuracy and reliability in fake currency detection.