Abstract: Counterfeit currency remains a significant challenge worldwide, posing threats to economic stability and security. This paper presents a novel approach for detecting fake currency utilizing image processing techniques. The proposed system leverages the advancements in computer vision and machine learning to automatically identify counterfeit banknotes with high accuracy. Initially, the input currency image is preprocessed to enhance its quality and extract relevant features. Subsequently, feature extraction algorithms are applied to capture distinctive patterns and characteristics unique to genuine banknotes. These features are then fed into a machine learning model, such as a neural network or support vector machine, trained on a dataset comprising both genuine and counterfeit currency samples. Through extensive experimentation and validation, the effectiveness of the proposed method is demonstrated in accurately distinguishing between authentic and counterfeit banknotes. The system's robustness against various types of counterfeit techniques and its potential for real-time application make it a promising tool for combating counterfeit currency fraud. This research contributes to the ongoing efforts in developing reliable and efficient solutions for safeguarding financial systems against counterfeit threats.
Keywords— Machine learning, Artificial learning, CNN and Image processing.
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DOI:
10.17148/IJARCCE.2024.134122
