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Counterfeit Currency Detection Using Machine Learning

Author

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  • Sonali B. Kanawade
  • Sammed S. Jangade
  • Abhishek R. Mane
  • Tejas D. Kurne

Abstract

Counterfeit currency detection is a critical aspect of maintaining financial integrity. This paper introduces an innovative methodology for identifying counterfeit Indian rupee notes. By employing advanced image processing techniques and Convolutional Neural Networks (CNNs), specifically ResNet architecture, the proposed system achieves impressive accuracy in distinguishing genuine from counterfeit banknotes. The dataset, meticulously compiled through a combination of web scraping and synthetic printing, ensures robust training and evaluation. Experimental findings underscore the efficacy of the ResNet-based approach, highlighting its superiority over traditional CNN models. This research contributes significantly to bolstering currency security and trust in financial transactions, offering practical insights for combating counterfeit currency challenges.

Suggested Citation

  • Sonali B. Kanawade & Sammed S. Jangade & Abhishek R. Mane & Tejas D. Kurne, 2024. "Counterfeit Currency Detection Using Machine Learning," International Journal of Scientific Research in Science, Engineering and Technology, Technoscience Academy, vol. 11(3), pages 399-405, May.
    • Handle: RePEc:ijs:ijsrse:v11:y2024:i3:id:149
    DOI: 10.32628/IJSRSET24113139
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