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Related Experiment Video

Updated: Nov 10, 2025

The Visual Colorimetric Detection of Multi-nucleotide Polymorphisms on a Pneumatic Droplet Manipulation Platform
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DeepMoney: counterfeit money detection using generative adversarial networks.

Toqeer Ali1, Salman Jan2, Ahmad Alkhodre1

  • 1Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah, Saudi Arabia.

Peerj. Computer Science
|April 5, 2021
PubMed
Summary

DeepMoney, a machine learning system using Generative Adversarial Networks (GANs), effectively identifies counterfeit Pakistani banknotes. This novel approach achieves 80% accuracy in distinguishing fake currency from genuine notes.

Keywords:
Counterfeit MoneyDeep LearningGenerative Adversarial Networks

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Conventional paper currency remains prevalent globally, despite the rise of electronic transactions.
  • Identifying forged currency is a growing challenge due to sophisticated counterfeiting methods.
  • Automated systems are needed to enhance the security and authenticity verification of paper money.

Purpose of the Study:

  • To develop a machine-assisted system, DeepMoney, for discriminating between genuine and counterfeit currency notes.
  • To leverage advanced machine learning techniques for improved banknote verification.
  • To address the increasing problem of currency counterfeiting.

Main Methods:

  • Utilized Generative Adversarial Networks (GANs), a state-of-the-art machine learning model, for training.
  • Employed unsupervised learning within GANs for model training, enabling supervised predictions.
  • Integrated advanced image processing and feature recognition techniques for input validation.
  • Applied the system to Pakistani banknotes, using augmented image samples for experimentation.

Main Results:

  • Developed a high-precision machine-assisted system (DeepMoney) for currency recognition.
  • Achieved an accuracy of 80% in distinguishing genuine from fake banknotes.
  • Demonstrated the effectiveness of GANs in banknote verification tasks.

Conclusions:

  • A machine-assisted system using GANs can effectively identify forged currency notes.
  • The DeepMoney system offers a promising solution for enhancing the security of paper currency.
  • The open-source availability of the code encourages further research and development in counterfeit detection.