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Prajakta Pawar,
Vaibhav Patil,
Abhishek Shitole,
Sujay Khodke,
Dhiraj Jagtap,
- Assistant Professor, Department of Computer Engineering, Bharati Vidyapeeth College of Engineering, Lavale, Pune, (SPPU), Maharashtra, India
- Student, Department of Computer Engineering, Bharati Vidyapeeth College of Engineering, Lavale, Pune, (SPPU), Maharashtra, India
- Student, Department of Computer Engineering, Bharati Vidyapeeth College of Engineering, Lavale, Pune, (SPPU), Maharashtra, India
- Student, Department of Computer Engineering, Bharati Vidyapeeth College of Engineering, Lavale, Pune, (SPPU), Maharashtra, India
- Student, Department of Computer Engineering, Bharati Vidyapeeth College of Engineering, Lavale, Pune, (SPPU), Maharashtra, India
Abstract document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_110315’);});Edit Abstract & Keyword
The production of counterfeit paper currencies has become cheaper due to advancements in printing technologies and graphic design. The circulation of fake currencies negatively impacts a country’s economy. This paper aims to develop an intelligent system for detecting and categorizing counterfeit currencies, which also plays a significant role in the field of Human-Computer Interaction (HCI). The paper delves into the history of counterfeit currencies and the development of techniques used from ancient to modern times to prevent and detect counterfeit paper bills. The ability to distinguish genuine bills from fake ones is not only a technological necessity but also a crucial safeguard for a nation’s financial stability. By leveraging cutting-edge technology, data analysis, and pattern recognition, various algorithms can significantly contribute to preserving the integrity of a country’s currency and financial systems. By analyzing high-frequency components, we can calculate relevant features that contain characteristics of specific patterns and allow for classification. We found that using statistical features is effective when analyzing bills. The recommended features were input into a classifier that successfully determined the authenticity of supplied bills.
Keywords: Counterfeit currency, Paper currency, SVM, Intelligent system, fake notes, bills, etc.
[This article belongs to Journal Of Network security (jons)]
Prajakta Pawar, Vaibhav Patil, Abhishek Shitole, Sujay Khodke, Dhiraj Jagtap. Deep Analysis of the Banknote Identification and Counterfeit Detection. Journal Of Network security. 2024; 12(03):-.
Prajakta Pawar, Vaibhav Patil, Abhishek Shitole, Sujay Khodke, Dhiraj Jagtap. Deep Analysis of the Banknote Identification and Counterfeit Detection. Journal Of Network security. 2024; 12(03):-. Available from: https://journals.stmjournals.com/jons/article=2024/view=0
References
document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_110315’);});Edit
- Finlay R, Francis A. A Brief History of Currency Counterfeiting| Bulletin–September 2019. Available from https://www.rba.gov.au/publications/bulletin/2019/sep/pdf/a-brief-history-of-currency-counterfeiting.pdf
- Thakur M, Kaur A. Various fake currency detection techniques. International Journal for Technological Research in Engineering. 2014 Jul;1(11):1309-13.
- Yan WQ, Chambers J, Garhwal A. An empirical approach for currency identification. Multimedia tools and applications. 2015 Jul;74:4723-33.
- Desai S, Kabade S, Bakshi A, Gunjal A, Yeole M. Implementation of multiple kernel support vector machine for automatic recognition and classification of counterfeit notes. Int. J. Sci. Eng. Res. 2014 Oct;5:882-6.
- Mann M, Shukla S, Gupta S. A comparative study on security features of banknotes of various countries. Int. J. Multidiscip. Res. Dev. 2015 Jun;2:83-91.
- Upadhyaya A, Shokeen V, Srivastava G, Drknmiet G. A Statistical Study on Banknote Counterfeit Detection. International Journal of Smart Business and Technology. 2016;4(1):19-26.
- Tandon S, Rao C. Evaluating the impact of Pillars One and Two. Research Paper; 2022. Available from https://www.econstor.eu/handle/10419/270392
- Nazir B, Imran M, Jehangir B. Banknote Verification using Image Processing Techniques. Journal of Computing & Biomedical Informatics. 2024 Jun 1;7(01):125-43.
- Banknote. 2024. Available from: https://en.wikipedia.org/wiki/Banknote
- Wang Z, Guo Q, Abdo HG. Research On Multi-Dimensional Image Processing In Intelligent Design Based On Perceptual Feature Quantification. Journal of Network Intelligence. 2024;9(2):1155-1167.
Journal Of Network security
| Volume | 12 |
| Issue | 03 |
| Received | 28/06/2024 |
| Accepted | 18/09/2024 |
| Published | 29/10/2024 |
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