Proceedings of the International Conference on Materials, Energy, Environment & Manufacturing Sciences & Computational Intelligence and Smart Communication (MEEMS-CISC-2024)

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A Deep Learning Approach for Enhanced Counterfeit Currency Detection and Classification

Authors
M. R. Pruthvi1, Y. M. Saumya1, *, B. Jaishma Kumari1, M. Prajna1, K. V. Jithya1
1St Joseph College of Engineering, Vamanjoor, India
*Corresponding author. Email: saumyam@sjec.ac.in
Corresponding Author
Y. M. Saumya
Available Online 16 June 2025.
DOI
10.2991/978-94-6463-762-5_2How to use a DOI?
Keywords
Counterfeit detection; Deep Convolution Neural Network (Deep CNN); fake currency; deep learning
Abstract

Counterfeit currency poses a significant threat to financial systems around the world. In this study, an innovative approach to coun- terfeit currency detection and classification is proposed leveraging the power of deep learning. The proposed system integrates deep learning models, each trained to identify intricate patterns and features inherent in genuine and counterfeit currency notes. These networks are trained on a comprehensive dataset comprising authentic and counterfeit currency images, allowing the system to learn and distinguish subtle visuals that are challenging for traditional methods. The deep learning approach further refines the detection process, providing a robust and adaptable solution capable of handling various counterfeiting techniques. The pro- posed deep learning approach represents a significant advancement in counterfeit currency detection, combining the strengths of deep neural networks.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Materials, Energy, Environment & Manufacturing Sciences & Computational Intelligence and Smart Communication (MEEMS-CISC-2024)
Series
Advances in Engineering Research
Publication Date
16 June 2025
ISBN
978-94-6463-762-5
ISSN
2352-5401
DOI
10.2991/978-94-6463-762-5_2How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - M. R. Pruthvi
AU  - Y. M. Saumya
AU  - B. Jaishma Kumari
AU  - M. Prajna
AU  - K. V. Jithya
PY  - 2025
DA  - 2025/06/16
TI  - A Deep Learning Approach for Enhanced Counterfeit Currency Detection and Classification
BT  - Proceedings of the International Conference on Materials, Energy, Environment & Manufacturing Sciences & Computational Intelligence and Smart Communication (MEEMS-CISC-2024)
PB  - Atlantis Press
SP  - 4
EP  - 13
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-762-5_2
DO  - 10.2991/978-94-6463-762-5_2
ID  - Pruthvi2025
ER  -