Fake Currency Identification System Using Convolutional Neural Network

Fake currency is a serious problem in India, and detecting it is crucial to maintain the integrity of the country's currency. There are various software programs available in India for detecting fake currency. These software programs use image processing techniques to analyze images of currency notes and identify any irregularities that indicate counterfeit notes. Existing methods for detecting counterfeit notes rely primarily on image processing techniques. A web-based detection system provides an easy-to-use interface for users to upload images of currency notes, send them to the CNN model (Venkata Raghu et al. in Int J Creat Res Thoughts (IJCRT) 10:2320–2882, 2004), and view the analysis results. Convolutional Neural Networks (CNNs) (Pallavi et al. in Int Res J Modernizat Eng Technol Sci 50:4076–4081, 2002) are used in the detection of counterfeit currency by analyzing the security features of currency notes and learning to differentiate between genuine and counterfeit notes based on those features. Using a real-time camera view (Selvi Rajendran and Anithaashri in IOP Conf Ser Mater Sci Eng 992:01201, 2020), this study will identify Indian banknotes by extracting features from notes, the model can identify counterfeit money. The model is trained with 80–20% training and test split, with each layer receiving the same learning rate of 0.001. After training the network for 200 cycles with 306 images, the training accuracy score 90.6%.