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This study introduces a faster machine learning method for joint banknote recognition and counterfeit detection. The explainable AI component enhances understanding of the system

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Current methods for banknote recognition and counterfeit detection are often serial, leading to inefficiencies.
  • There is a need for faster and more integrated solutions in currency processing.

Purpose of the Study:

  • To develop a machine learning-based method that performs joint banknote recognition and counterfeit detection simultaneously.
  • To introduce an explainable artificial intelligence (XAI) component for visualizing model decision-making processes.
  • To improve computational efficiency compared to existing serial approaches.

Main Methods:

  • Implementation of a novel machine learning algorithm for integrated banknote recognition and counterfeit detection.
  • Development of an XAI technique to visualize feature importance for recognition and detection.
  • Experimental validation using United States Dollar and European Union Euro banknotes.

Main Results:

  • The proposed joint method significantly reduces computation time compared to conventional serial methods.
  • The XAI visualization provides insights into the regions of banknotes critical for accurate recognition and detection.
  • Achieved high accuracy in both recognizing banknote types and identifying counterfeits.

Conclusions:

  • Simultaneous processing of banknote recognition and counterfeit detection via machine learning offers substantial speed improvements.
  • Explainable AI enhances the transparency and trustworthiness of machine learning systems in financial applications.
  • The developed method presents a viable and efficient solution for currency authentication.