Deep Analysis of the Banknote Identification and Counterfeit Detection

Year : 2024 | Volume : 12 | Issue : 03 | Page : 13-17
    By

    Prajakta Pawar,

  • Vaibhav Patil,

  • Abhishek Shitole,

  • Sujay Khodke,

  • Dhiraj Jagtap,

  1. Assistant Professor, Department of Computer Engineering, Bharati Vidyapeeth College of Engineering (Savitribai Phule Pune University), Lavale, Pune, Maharashtra, India
  2. Student, Department of Computer Engineering, Bharati Vidyapeeth College of Engineering (Savitribai Phule Pune University), Lavale, Pune, Maharashtra, India
  3. Student, Department of Computer Engineering, Bharati Vidyapeeth College of Engineering (Savitribai Phule Pune University), Lavale, Pune, Maharashtra, India
  4. Student, Department of Computer Engineering, Bharati Vidyapeeth College of Engineering (Savitribai Phule Pune University), Lavale, Pune, Maharashtra, India
  5. Student, Department of Computer Engineering, Bharati Vidyapeeth College of Engineering (Savitribai Phule Pune University), Lavale, Pune, Maharashtra, India

Abstract

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

[This article belongs to Journal Of Network security ]

How to cite this article:
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):13-17.
How to cite this URL:
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):13-17. Available from: https://journals.stmjournals.com/jons/article=2024/view=180528


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Regular Issue Subscription Review Article
Volume 12
Issue 03
Received 28/06/2024
Accepted 18/09/2024
Published 29/10/2024
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