International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Identifying and Detecting Currency through Image Processing with Convolutional Neural Network

Abstract

Bank currency is our nation's most valuable asset, and in order to cause financial inconsistencies, counterfeit notes that seem like the real thing are introduced into the financial market. During demonetization time it is seen that so much of currency is floating in market. In general, by a human being, it is very difficult to identify forged note from the genuine not instead of various parameters designed for identification as many features of forged note are similar to original one. To discriminate between fake bank currency and original note is a challenging task. So, there must be an automated system that will be available in banks or in ATM machines. To design such an automated system there is need to design an efficient algorithm which is able to predict weather the banknote is genuine or forged bank currency as fake notes are designed with high precision. This paper proposes a CNN algorithm-based fake currency detection model for authenticating Indian currency notes with denominations of 10, 20, 50, 200, and 500. The results are also fairly good and also proposed model has the accuracy of 99.3%.

Country : India

1 Moksud Alam Mallik2 Mohammed Abdul Mubashir3 Sameena Sultana4 Mirza Younus Ali Baig5 Md Ashique Hussain

  1. Dean R&D, Associate Professor, Department of CSE (Data Science), Lords Institute of Engineering and Technology, Hyderabad, India
  2. UG Student, Department of CSE (Data Science), Lords Institute of Engineering and Technology, Hyderabad, India
  3. UG Student, Department of CSE (Data Science), Lords Institute of Engineering and Technology, Hyderabad, India
  4. Assistant Professor, Department of CSE (Data Science), Lords Institute of Engineering and Technology, Hyderabad, India
  5. Assistant Professor, Department of CSE (Data Science), Lords Institute of Engineering and Technology, Hyderabad, India

IRJIET, Volume 9, Special Issue of INSPIRE’25 April 2025 pp. 328-336

doi.org/10.47001/IRJIET/2025.INSPIRE53

Full Paper
Download

References

  1. Zhang, Q. and Yan, W.Q. (2018) ‘Currency detection and recognition based on Deep Learning’, 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6. doi:10.1109/avss.2018.8639124.
  2. Kumar, Dr .P. et al. (2022) ‘Detection of fake currency using KNN algorithm’, International Journal for Research in Applied Science and Engineering Technology, 10(5), pp. 2328–2335. doi:10.22214/ijraset.2022.42829.
  3. “Collective collecting in ”the age of associations” (2018) Collecting and Historical Consciousness in Early Nineteenth-Century Germany, pp. 60–104. doi:10.7591/9781501723599-004.
  4. Suneetha Rani R et al. (2022) ‘Detecting counterfeit banknotes with machine learning’, South Asian Journal of Engineering and Technology, 12(3), pp. 146–151. doi:10.26524/sajet.2022.12.40.
  5. Yadav, A. et al. (2021) ‘Evaluation of machine learning algorithms for the detection of fake bank currency’, 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 810–815. doi:10.1109/confluence51648.2021.9377127.
  6. Jain, S. (2021) ‘Mere use and possession of forged or counterfeit currency notes and bank notes: A penal offence?’, SSRN Electronic Journal [Preprint]. doi:10.2139/ssrn.3902384.
  7. ‘Algorithm based security system for banknotes’ (2018) International Journal of Recent Trends in Engineering and Research, pp. 231–242. doi:10.23883/ijrter.conf.20171225.036.xwarv.
  8. Kamble, K. et al. (2019) ‘Counterfeit currency detection using deep convolutional neural network’, 2019 IEEE Pune Section International Conference (PuneCon), pp. 1–4. doi:10.1109/punecon46936.2019.9105683.
  9. ‘Detection of Indian counterfeit banknotes using neural network’ (2018) International Journal of Recent Trends in Engineering and Research, 4(12), pp. 77–82. doi:10.23883/ijrter.2018.4425.nb6dm.
  10. ‘Supervised learning—classification using logistic regression’ (2019) Python® Machine Learning, pp. 151–175. doi:10.1002/9781119557500.ch7.
  11. ‘Feature Extraction in Image Processing: Techniques and Applications’ (2019) Python® Machine Learning, pp. 151–175. doi:10.1002/9781119557500.ch7.
  12. ‘COUNTERFEIT CURRENCY IDENTIFICATION THROUGH IMAGE PROCESSING METHODS’ (2019) Python® Machine Learning, pp. 151–175. doi:10.1002/9781119557500.ch7.
  13. ‘Currency detection using image processing’ (2019) Python® Machine Learning, pp. 151–175. doi:10.1002/9781119557500.ch7.
  14. ‘Recognition of Currency Based on Security Thread Feature of Currency’ (2019) Python® Machine Learning, pp. 151–175. doi:10.1002/9781119557500.ch7.
  15. ‘Android Based Currency Recognition System for Blind’ (2019) Python® Machine Learning, pp. 151–175. doi:10.1002/9781119557500.ch7.
  16. 16. ‘Indian Currency Classification and Counterfeit Detection Using Deep Learning and Image Processing Approach’ (2019) Python® Machine Learning, pp. 151–175. doi:10.1002/9781119557500.ch7.
  17. ‘Counterfeit Currency Detection using Deep Convolutional Neural Network ’ (2019) Python® Machine Learning, pp. 151–175. doi:10.1002/9781119557500.ch7.
  18. ‘Fake Currency Detection with Machine Learning Algorithm and Image Processing’ (2019) Python® Machine Learning, pp. 151–175. doi:10.1002/9781119557500.ch7.
  19. ‘Predictive Power of CNN-SVM in Fake Currency Detection Analysis’ (2019) Python® Machine Learning, pp. 151–175. doi:10.1002/9781119557500.ch7.
  20. ‘Predictive Power of CNN-SVM in Fake Currency Detection Analysis’ (2019) Python® Machine Learning, pp. 151–175. doi:10.1002/9781119557500.ch7.
  21. ‘Fake Currency Detection Across Five Classes: A CNN-SVM Based Approach’ (2019) Python® Machine Learning, pp. 151–175. doi:10.1002/9781119557500.ch7.
  22. ‘CNN based Counterfeit Indian Currency Recognition Using Generative Adversarial Network’ (2019) Python® Machine Learning, pp. 151–175. doi:10.1002/9781119557500.ch7.
  23. S. R. Darade and G. Gidveer, “Automatic recognition of fake Indian currency note,” in 2016 International Conference on Electrical Power and Energy Systems (ICEPES). IEEE, 2016, pp. 290–294.
  24. B. P. Yadav, C. Patil, R. Karhe, and P. Patil, “An automatic recognition of fake Indian paper currency note using MATLAB,” Int. J. Eng. Sci. Innov. Technol, vol. 3, pp. 560–566, 2014.
  25. A.Zarin and J. Uddin, “A hybrid fake banknote detection model using OCR, face recognition and Hough features,” in 2019 Cybersecurity and Cyberforensics Conference (CCC). IEEE, 2019, pp. 91–95.
  26. M. S. Veling, M. J. P. Sawal, M. S. A. Bandekar, M. T. C. Patil, and M. A. L. Sawant, “Fake Indian currency recognition system by using MATLAB.”
  27. F. A. B, P. Mangayarkarasi, Akhilendu, A. A. S, and M. K, “Fake Indian currency note recognition,” vol. 7, pp. 4766–4770, 2020. [Online]. Available: IRJET Paper.
  28. DnyaneshRP/Fake-Currency-Detection-Using-CNN: This GitHub repository provides a project on fake Indian currency detection using CNN. It includes detailed information about the implementation and features of the project.
  29. IDENTIFICATION OF CURRENCY BY IMAGE PROCESSING AND CNN TECHNIQUES: This paper discusses the identification of currency using image processing and CNN techniques. It covers the methodology, dataset, and results of the study.
  30. DETECTION OF FAKE CURRENCY USING IMAGE PROCESSING: This paper presents a fake currency detection system designed for Android devices using image processing and machine learning techniques.

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies