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A deep learning-based system for detecting counterfeit currency that is specific to Indian banknotes is proposed in this work. We create a custom dataset of real and fake notes across several denominations, use augmentation to address imbalance, and assess transfer learning models (AlexNet, InceptionV3), in contrast to previous works that either use small datasets or lack deployment. InceptionV3 outperformed AlexNet with an F1-score of 97.24% and 99 % validation accuracy. Real-time detection from webcam input or uploaded images is made possible by a Flask-based web application. Our contribution is to provide a lightweight, precise, and easily accessible solution that bridges the gap between research and deployment. The dataset will be expanded, multi-currency support will be integrated, and sophisticated counterfeiting techniques will be addressed in future work.

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Smart Detection of Indian Counterfeit Currency Notes using Deep Learning Techniques

  • 会议论文

  • 发表日期：

    2026年

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  • Laavanya Mohan,
  • Visali Janga,
  • Sai Vinay Chode,
  • Vijayaraghavan Veeramani

摘要

A deep learning-based system for detecting counterfeit currency that is specific to Indian banknotes is proposed in this work. We create a custom dataset of real and fake notes across several denominations, use augmentation to address imbalance, and assess transfer learning models (AlexNet, InceptionV3), in contrast to previous works that either use small datasets or lack deployment. InceptionV3 outperformed AlexNet with an F1-score of 97.24% and 99 % validation accuracy. Real-time detection from webcam input or uploaded images is made possible by a Flask-based web application. Our contribution is to provide a lightweight, precise, and easily accessible solution that bridges the gap between research and deployment. The dataset will be expanded, multi-currency support will be integrated, and sophisticated counterfeiting techniques will be addressed in future work.