Detecting Counterfeit Currency with Advanced Machine Learning and Image Processing

Counterfeiting poses a great danger to the financial system for fake notes look similar to genuine notes and it is impossible toidentify them manually. During an incident like demonetization, the flow of counterfeit money is felt suddenly and discrepancystarts cropping up in economy. To solve this problem, an automatic system can be used to discriminate between genuine andforgery banknotes. In this paper, we introduce the use of a CNN (which is deep learning algorithm) to come up with an effectivecounterfeit detection system. For this study, a UCI Machine Learning Repository dataset which contains labeled images of thebanknote have been used for training and testing the model. CNNs, which are adept at learning intricate features of an image data,match for detecting the subtle differences between counterfeit and genuine notes. The proposed system is ex- ported in terms of accuracy, precision, recall and F1 measure to classify a banknote to its own class being genuine or counterfeit. The experiments demonstrate the viability of the CNN-based models to automate banknote recognition, making it a reliable tool to be used in the banks and ATMs. Such safeguards could massively improve the security, diminish the flow of counterfeit money and protect the financial environment.