Smart Fake Currency Detection System using Machine Learning
DOI:
https://doi.org/10.15662/IJEETR.2026.0802122Keywords:
Fake Currency Detection, Machine Learning, Image Processing, RGB Color Analysis, Texture Analysis, Security Feature Detection, Canny Edge Detection, Laplacian Variance, Feature Extraction, Pattern Recognition, Computer Vision, Currency Authentication, Hybrid Classification, Automated Detection SystemAbstract
The rapid growth of counterfeit currency circulation has become a major concern for financial institutions, retail systems, and economic stability. Traditional currency authentication techniques, including manual inspection and hardware-based methods such as ultraviolet (UV) and infrared (IR) detection, are often expensive, time-consuming, and require trained personnel. These limitations highlight the need for an automated, cost-effective, and scalable solution for reliable counterfeit detection
This paper presents a smart fake currency detection system using a hybrid machine learning framework integrated with advanced image processing techniques. The proposed system adopts a non-destructive, image-based approach that analyzes currency notes captured using standard cameras or mobile devices. The system processes the input image through multiple stages, including preprocessing, feature extraction, and classification, to accurately determine the authenticity of the note
In the preprocessing stage, the input image is resized and enhanced using Gaussian filtering to reduce noise and ensure consistency. The system then extracts multiple discriminative features from both RGB and grayscale domains. RGB color variation analysis is performed to evaluate ink distribution and detect abnormalities in color consistency, which are common in counterfeit notes
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