CurrencyShield: A Survey of Deep Learning Applications in Currency Risk Mitigation

Abstract

The widespread issue of counterfeit currency poses a major challenge to the economy, undermining public trust and affecting financial stability. This paper introduces a comprehensive Fake Currency Detection System specifically tailored for Indian banknotes, utilizing advanced deep learning and image processing techniques to accurately distinguish between genuine and counterfeit notes. The proposed system employs a Convolutional Neural Network (CNN) architecture to analyse critical visual features of currency notes, such as the image of Mahatma Gandhi, serial numbers, and security strips, which are key indicators of note authenticity. By focusing on these security elements, the model can detect minor alterations or missing features that are often present in counterfeit currency. The system is implemented as a web-based application, developed using Python and the Flask framework, which provides a simple and interactive platform for users to upload images of currency notes for verification. Upon image submission, the CNN model processes the input and classifies the note as either real or fake with high precision and accuracy. To enhance usability and accessibility, the system offers multilingual voice feedback in English, Hindi, and Marathi, catering to a broad user base, including individuals with visual impairments. This feature ensures effective communication and ease of use across different linguistic communities in India.