Abstract :

The Proposed System Tackles The Challenge Of Counterfeit Currency Detection By Leveraging Advanced Image Analysis And Deep Learning Techniques. Utilizing A Convolutional Neural Network (CNN), The System Automatically Examines Currency Note Images For Critical Visual Features Such As Texture, Edges, And Embedded Security Elements To Classify Notes As Real Or Fake. To Enhance User Trust And Model Interpretability, Explainable AI Methods Like Grad-CAM Are Integrated, Visually Indicating Which Parts Of The Note Influenced The Classification Decision. This Transparency Aids Users In Understanding And Validating The Model S Predictions. The Implementation Employs Python-based Technologies Including TensorFlow And Keras For Building And Training The CNN, OpenCV And NumPy For Image Preprocessing, And Flask Combined With HTML/CSS To Create An Accessible Web Interface. Users Can Upload Images Of Currency Notes And Receive Instant Verification Results. This Solution Offers A Practical, Scalable, And Costeffective Alternative To Traditional Manual Or Hardware-dependent Counterfeit Detection, Contributing To Improved Economic And Financial Security.

Published:

31-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

1498 - 1506

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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