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Financial institutions throughout the world are becoming increasingly concerned about detecting counterfeit cash. This work presents a reliable detection system that uses image processing methods and machine learning algorithms to accurately identify counterfeit currency. Before being classified using logistic regression, banknote pictures are preprocessed using OpenCV for feature extraction. The system examines user-input photos and outputs results in real time when integrated into a web interface built with Django. Variance, entropy, Gaussian blurring, and Sobel edge detection are important preprocessing techniques for fine-tuning image characteristics. Metrics like the confusion matrix and precision-recall are used to assess the system’s high accuracy and performance. Blockchain integration for safe transaction monitoring is one of the next improvements.

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Real-Time Fake Currency Detection through Feature Extraction and ML Classifiers

  • 会议论文

  • 发表日期：

    2026年

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  • K. S. Shashikala,
  • Sandyarani Vadlamudi,
  • Siya Raj,
  • Sudiksha Ravipati,
  • Parimitha Manigani,
  • S. Somesh

摘要

Financial institutions throughout the world are becoming increasingly concerned about detecting counterfeit cash. This work presents a reliable detection system that uses image processing methods and machine learning algorithms to accurately identify counterfeit currency. Before being classified using logistic regression, banknote pictures are preprocessed using OpenCV for feature extraction. The system examines user-input photos and outputs results in real time when integrated into a web interface built with Django. Variance, entropy, Gaussian blurring, and Sobel edge detection are important preprocessing techniques for fine-tuning image characteristics. Metrics like the confusion matrix and precision-recall are used to assess the system’s high accuracy and performance. Blockchain integration for safe transaction monitoring is one of the next improvements.