FAKE CURRENCY DETECTION USING IMAGE PROCESSING

Abstract

Abstract: Counterfeit currency is a serious problem that can cause financial loss and affect the reliability of monetary transactions. Manual verification of currency notes can be difficult and time-consuming, especially when security features are not easy to identify. This paper presents a fake currency detection approach using image processing techniques. The proposed system is developed using Python and uses OpenCV, NumPy, and Pillow for image processing and image analysis. The user provides an image of a currency note through the application. The uploaded image is resized and processed to obtain useful visual information. Image processing operations such as grayscale conversion, noise reduction, edge detection, color analysis, texture analysis, and image comparison are used to examine the characteristics of the currency note. Features such as the overall appearance, edges, texture, watermark-related patterns, security-thread-related regions, and serial-number-related information can be considered during the verification process. The extracted information is compared with the characteristics of a genuine reference note, and a result is generated to indicate whether the input note is considered genuine or fake. The proposed approach provides a simple computer-based method for assisting currency verification through image analysis and can be further improved by using larger datasets, additional security features, and advanced machine learning techniques.