Deep Learning Model for Indian Fake Currency Detection

Detecting counterfeit banknotes is a key problem facing many countries. In India, the problem of counterfeit banknotes has increased in recent years, causing huge economic losses. In this study, a new approach is proposed to detect counterfeit Indian banknotes using deep convolutional neural networks (CNNs). The proposed approach involves using datasets of genuine and counterfeit Indian banknotes. This dataset was created by collecting high-resolution images of real and counterfeit banknotes from various sources. Images have been pre-processed to remove noise and improve quality. We then used the pre-processed images to train a deep VGG16 model. The model is designed to learn the characteristics of genuine banknotes and distinguish them from counterfeit banknotes. The trained model was tested on separate datasets of real and counterfeit banknotes and its performance was evaluated using various metrics. Research results show that the proposed approach can effectively detect counterfeit Indian banknotes with high accuracy. The Deep VGG16 model achieves over 99% accuracy in identifying counterfeit banknotes, outperforming existing approaches. Overall, the proposed approach can be used as a reliable and efficient tool for detecting counterfeit Indian banknotes and mitigating economic losses caused by the prevalence of counterfeit banknotes.