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The increasing prevalence of counterfeit currency poses a serious challenge to economic stability, making advanced detection techniques crucial. This project, titled “Detection of Fake Indian Currency Using Convolutional Neural Networks”, presents an innovative method for identifying counterfeit notes through deep learning. The research investigates three core models: MobileNet, a hybrid model combining MobileNet with Support Vector Machines (SVM), and a more complex hybrid that integrates MobileNet, SVM, and Random Forest. The project leverages MobileNet for their known efficiency and accuracy in image classification, assessing their effectiveness in distinguishing between genuine and counterfeit Indian currency. By merging MobileNet with SVM, the hybrid model aims to improve detection accuracy and manage the complexity of counterfeit note patterns. The enhanced version, integrating both SVM and Random Forest, seeks to further boost classification accuracy using ensemble learning techniques. The findings highlights the potential of convolutional neural networks in advancing counterfeit currency detection, offering crucial insights for enhancing financial security systems.

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Counterfeit Indian Currency Detection with CNN: A Deep Learning Approach

  • 会议论文

  • 发表日期：

    2025年

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  • R. Tamilkodi,
  • S. Ratalu,
  • R. Ravichand,
  • A. Janardhan,
  • V. Niranjan,
  • A. Deekshith Kumar

摘要

The increasing prevalence of counterfeit currency poses a serious challenge to economic stability, making advanced detection techniques crucial. This project, titled “Detection of Fake Indian Currency Using Convolutional Neural Networks”, presents an innovative method for identifying counterfeit notes through deep learning. The research investigates three core models: MobileNet, a hybrid model combining MobileNet with Support Vector Machines (SVM), and a more complex hybrid that integrates MobileNet, SVM, and Random Forest. The project leverages MobileNet for their known efficiency and accuracy in image classification, assessing their effectiveness in distinguishing between genuine and counterfeit Indian currency. By merging MobileNet with SVM, the hybrid model aims to improve detection accuracy and manage the complexity of counterfeit note patterns. The enhanced version, integrating both SVM and Random Forest, seeks to further boost classification accuracy using ensemble learning techniques. The findings highlights the potential of convolutional neural networks in advancing counterfeit currency detection, offering crucial insights for enhancing financial security systems.