Counterfeit Buster: Indian Currency Detection Using Generative Adversarial Networks

Counterfeit currency undermines financial trust and economic stability, particularly in cash- dependent regions. As counterfeiters adopt increasingly sophisticated techniques, forged notes can evade visual inspection, rendering traditional detection methods unreliable. Furthermore, existing verification techniques are typically hardware-dependent, expensive, or too complex for general users, making them impractical and inaccessible in real-world applications. To overcome these limitations, we introduce Counterfeit Buster, an Android application that leverages Generative Adversarial Networks (GANs) for real-time detection of counterfeit Indian currency. GANs operate by training two neural networks—a generator that produces synthetic data and a discriminator that learns to distinguish it from real data—thereby enhancing detection accuracy through continuous learning. The app allows users to scan currency using their smartphone camera and receive instant results, offering a portable, affordable, and user-friendly solution. Additional features such as denomination recognition, real-time currency conversion, and voice feedback further improve accessibility and usability, empowering users to effectively combat currency fraud.

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Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2025-05-14
DOI
https://doi.org/10.22214/ijraset.2025.70781
Primary Topic
Currency Recognition and Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Counterfeit Buster: Indian Currency Detection Using Generative Adversarial Networks

Hrutali Dhawade
International Journal for Research in Applied Science and Engineering Technology
Currency Recognition and Detection
article

Counterfeit Buster: Indian Currency Detection Using Generative Adversarial Networks

Hrutali Dhawade
article en

Abstract

Counterfeit currency undermines financial trust and economic stability, particularly in cash- dependent regions. As counterfeiters adopt increasingly sophisticated techniques, forged notes can evade visual inspection, rendering traditional detection methods unreliable. Furthermore, existing verification techniques are typically hardware-dependent, expensive, or too complex for general users, making them impractical and inaccessible in real-world applications. To overcome these limitations, we introduce Counterfeit Buster, an Android application that leverages Generative Adversarial Networks (GANs) for real-time detection of counterfeit Indian currency. GANs operate by training two neural networks—a generator that produces synthetic data and a discriminator that learns to distinguish it from real data—thereby enhancing detection accuracy through continuous learning. The app allows users to scan currency using their smartphone camera and receive instant results, offering a portable, affordable, and user-friendly solution. Additional features such as denomination recognition, real-time currency conversion, and voice feedback further improve accessibility and usability, empowering users to effectively combat currency fraud.

International Journal for Research in Applied Science and Engineering TechnologyVol. 13(5)
MIT Art, Design and Technology University (IN)
Openalex Percentile: Top 91%
Currency Recognition and Detection
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