Fake-Indian-Currency-Detection with Deep Learning Based-Xception CNN

Abstract

Counterfeit money is still a major problem for banks and the economy. With the growing complexity of counterfeiters, conventional ways of identifying fake money are less effective. The current research introduces a deep learning-based approach for identifying fake Indian currency notes of ?100, ?200, and ?500 denominations. With the help of the Xception model, which is good at detecting minute image details, the system becomes adept at distinguishing subtle differences between real and fake notes. The model is trained on a data set with images of genuine and counterfeit notes, enabling it to learn intricate patterns and subtle differences that tend to be difficult to spot using the naked eye. Experiments show that the Xception-based model achieve superior classification accuracy and offers an efficient productive solution for high-speed, accurate, and automated counterfeiting detection in real-world scenarios. A range of data such as images of genuine and fake banknotes under various conditions are used in an attempt to enhance the robustness of the model. The proposed system demonstrates high accuracy and reliability, indicating that it can be applied to real-time counterfeit detection systems for enhanced security and confidence in financial transactions

Introduction

Counterfeiting of Indian currency, especially ?100, ?200, and ?500 notes, is a serious and growing problem that threatens financial systems and economic trust. Traditional manual detection methods (like UV scanning and watermark verification) are time-consuming and prone to errors, particularly against high-quality fakes. This creates a need for an automated, accurate counterfeit detection system.

Deep learning, particularly Convolutional Neural Networks (CNNs), offers strong potential for this task. The Xception model—a CNN architecture using depthwise separable convolutions—is highly effective for distinguishing subtle features in images and is well-suited for counterfeit detection. It leverages transfer learning, allowing it to be fine-tuned with a relatively small dataset of currency images taken under varied conditions.

The project aims to develop a robust counterfeit detection system using Xception, focusing on ?100, ?200, and ?500 notes. It involves building a balanced dataset of genuine and fake notes, applying preprocessing and augmentation to improve model generalization, optimizing the model, and evaluating its performance with metrics like precision and recall. The system is intended for practical use in banks, shops, and ATMs.

Challenges include detecting very high-quality fakes, handling genuine note variations (due to aging or wear), and dealing with environmental factors affecting image capture. Effective data collection, preprocessing, and augmentation are critical to overcome these issues.

The text also reviews related research where various machine learning and deep learning methods—such as CNNs, ensemble learning, transfer learning, image processing techniques, and multi-spectral imaging—have been applied to counterfeit currency detection with promising results.

The methodology section details the dataset creation, image preprocessing (resizing, normalization, augmentation), and real-time webcam image handling to ensure consistency with model requirements. The system uses binary classification to label notes as real or fake, simplifying model design and improving accuracy.

Conclusion

This project shows what powerful image processing methods can do in extracting significant features from the currency bills and facilitating correct differentiation between real and counterfeit bills. With folding networks (CNNS), the system significantly enhances the accuracy of classification and is a trustable method of practical deployment. Due to the feature extraction coupled with deep learning methods the project offers an effective solution to the present problems of currency forgery. In addition, the model performance indicates that AI-driven solutions are capable of substantially enhancing the accuracy and speed of counterfeit detection compared to conventional manual processes with low accuracy and timeliness. The combination of strong trait extraction and deep learning techniques presents an overwhelming solution to the ramp-stretching problem of currency tampering. This technology can significantly utilize usage from financial institutions who want to make automation and cash processing more efficient to combat cases with fake currencies. Additionally, implementing solutions on mobile devices allows checking on the go, making daily transactions more accessible and usable.

References

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Copyright

Copyright © 2025 Chaitra B, Keerthi P, Sonal C V, Tejaswini K Gowda , Akshatha Preeth P. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.