Abstract: The widespread circulation of counterfeit currency poses a significant threat to global financial stability and integrity, necessitating effective detection measures. In response to this challenge, this project aims to develop a robust and efficient system for automated counterfeit currency detection, utilizing deep learning methodologies. This research presents a comparative analysis focusing specifically on Indian 500 rupee notes, employing machine learning techniques such as Simple Neural Network (NN) and Deep Learning Convolutional Neural Network (CNN). Diverse datasets comprising images of Indian 500 rupee notes sourced from various outlets are utilized for this study. Performance metrics including accuracy, precision, recall, and F1-score are systematically computed for each technique to evaluate detection effectiveness. Results demonstrate the superiority of the CNN-based approach over the NN method, showcasing higher accuracy and robustness in identifying counterfeit Indian 500 rupee notes. This research significantly contributes to the advancement of automated counterfeit detection systems, particularly within the context of Indian currency. By enhancing detection capabilities and strengthening fraud prevention mechanisms, this work aims to bolster financial security measures on a global scale.
Keywords: Indian fake currency detection, simple neural network, convolutional neural network, image classification, counterfeit detection, financial security.
Cite:
Mr. K Bhushanm, M.Asritha, P.Rafiya Sultana, P.Anil Kumar, S.Mahesh Babu, "Fake Currency Detection using Deep Learning", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13339.
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DOI:
10.17148/IJARCCE.2024.13339
