Counterfeit Regulation through Machine Learning Approach and Deployment in Dockers
Ajit Kumar Rout, Abhishek Shety, Koushik Modekurti · 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) · 2022
Money is very essential in today’s world to lead a good & prosperous life. Money Mints are responsible for the printing of banknotes for a Nation. Dissimilitude here mentioned refers to unrestrained reproduction of currency with the help of advanced technology accessible to all. The act of manufacturing replicated currency without the authenticity of the State is called Counterfeiting. Fake Notes are being produced with many features that can’t be easily recognized by us and are similar to original Bank Notes. Therefore, an efficient mechanism is to be engineered for regulating the Counterfeit of Money. This paper implements Wavelet Transformed images of banknotes. Supervised Learning Classifiers, Random Forest & Naive Bayes are compared & used to predict original one. Performance Evaluation is based on parameters such as Accuracy. A User-Friendly Interface is designed using Flask, Flagger to view the predictions made by the Model and Docker for environment standardization.