Detection Of Fake Bank Currency Using Machine Learning

The development of colour printing technology has accelerated the production of counterfeit currency notes and their mass duplication. Counterfeiting of banknotes poses a significant threat to the financial system and undermines the integrity of currency .Image analysis to distinguish genuine banknotes from counterfeit ones based on distinctive features. Various image processing algorithms are then employed to extract key features, such as texture, colour, and security elements embedded in the banknotes. These features are crucial for differentiating authentic currency from counterfeit replicas .Machine learning algorithms, particularly deep neural networks, are employed to train a robust classification model using a labeled dataset containing examples of both genuine and fake banknotes. The model is designed to generalize well to unseen data, allowing for accurate detection of counterfeit currency across various denominations and designs. The proposed fake banknote detection system demonstrates promising results in terms of accuracy, speed, and scalability. It offers a reliable and efficient solution to financial institutions and law enforcement agencies for identifying counterfeit currency in real-time, thereby contributing to the preservation of the integrity of financial transactions and safeguarding the economy against illicit activities.