Fake Currency Detection Using Convolutional Neural Network and Image Processing

The increased use of modern printing and scanning technologies has led to a significant rise in counterfeit currency production, posing a serious threat to global economies. To tackle this growing issue, our project, titled “Fake currency detection using Convolutional Neural Networks and Image Processing,” introduces an innovative solution that utilizes artificial intelligence (AI) and machine learning for efficient counterfeit detection. Financial institutions, banks, and businesses are facing heightened vulnerability to counterfeit currency, resulting in considerable financial losses and a decrease in the value of genuine money. Current currency detection systems often rely on time-consuming traditional methods and manual inspection, which are prone to human error. Even the counterfeit detection machines in use have limitations when it comes to identifying sophisticated counterfeit notes. Our project addresses these challenges by proposing an advanced system that integrates convolutional neural networks (CNNs) and image processing techniques. Given the advancements in printing and scanning technologies, counterfeiting has evolved into a more sophisticated and widespread problem. Traditional currency detection methods, rooted in hardware and image processing, have proven to be inefficient and time-consuming. Hence, there is a critical need for a more robust and rapid solution to detect counterfeit currency. Our proposed approach employs a transfer-learned CNN, a deep learning model trained on a dataset comprising real and fake currency images. The CNN learns the intricate features of both genuine and counterfeit banknotes, allowing it to accurately identify fake currency in real-time. The transfer learning process enables the CNN to leverage knowledge gained from a diverse dataset, improving its ability to recognize subtle patterns associated with counterfeit notes. The primary components of our project include a diverse dataset with images of real and fake currency notes. This dataset is divided into training, validation, and test sets, totaling 575 images and covering various denominations and currencies. The CNN architecture is crafted to extract relevant features from these images, facilitating precise classification. Implemented as a web-based application for enhanced accessibility, our system allows users to upload images of banknotes through a user-friendly interface. The underlying AI model processes and analyzes these images, providing real-time feedback to indicate whether the currency note is genuine or fake. This user-friendly approach ensures easy integration into existing processes in financial institutions and businesses. Our project tackles the urgent issue of counterfeit currency detection through the integration of advanced AI and machine learning techniques. By combining CNNs and image processing, we aim to offer financial institutions and businesses a reliable and efficient tool to combat the growing threat of fake-currency, contributing to the protection of economies and financial systems.