Neural Guard: A Deep Learning Framework for Currency Fraud Detection

This review paper explores the increasing utilisation of deep learning techniques in currency verification, with a focus on identifying and preventing counterfeit Indian rupees. Counterfeit currency continues to pose a significant danger to the economic stability of nations globally, including India. Conventional detection approaches are constrained by their reliance on manual examination and their vulnerability to human error, rendering them ineffective for extensive or real-time applications. Deep learning has emerged as an effective option, utilising sophisticated algorithms to effectively detect counterfeit currency with minimal human involvement. The research methodically analyses various deep learning and machine learning models that demonstrate potential in currency verification, especially in the precise and efficient detection of counterfeit notes. Prominent among these are Convolutional Neural Networks (CNNs), recognised for their formidable image-processing skills. Convolutional Neural Networks (CNNs) can scrutinise intricate visual patterns in cash, rendering them exceptionally adept at detecting nuanced distinctions between authentic and counterfeit notes. Moreover, Support Vector Machines (SVM) and Random Forest algorithms are emphasised for their efficacy in classifying money photos based on various criteria. Recent and advanced models, like MoBiNet and ResNet-50, are examined, emphasising their ability to perform real-time money authentication tasks. The review examines the potential of these technologies to assist visually impaired individuals by facilitating automated currency identification systems that audibly verify the authenticity of banknotes.