The advent of state-of-the-art techniques and technologies has exposed human beings to some threats as well, apart from providing several advantages. Fake currency is such a major threat to society which in turn obstructs the country’s economic growth and financial status and causes an impact on human life. In addition to it, detecting fake currency is very challenging for the human eye. Detection of such currency is an important task and also consumes a lot of time if done manually. To address this critical issue, our paper contributes to identifying fake currency using Machine learning (ML) algorithms. In our article, we experimented with multiple ML algorithms to detect fake currencies and analyze their performance. Among the six algorithms experimented with for our work, Random Forest has shown the highest accuracy with 99.27% and a precision of 0.99 compared to the other algorithms. Additionally, we have demonstrated the interpretability of the ML model using LIME and SHAP which would be beneficial in understanding the important factors behind the particular prediction. We also performed hyperparameter tuning using GridSearchCV and manually too which showed us an improved accuracy performance of 99.99% from 99.27% for Random Forest. Feature Importance analysis and Explainability concepts are also demonstrated to provide an in-depth analysis of model prediction.

An Explainable Machine Learning Model to Analyze and Detect Fake Currency

The advent of state-of-the-art techniques and technologies has exposed human beings to some threats as well, apart from providing several advantages. Fake currency is such a major threat to society which in turn obstructs the country’s economic growth and financial status and causes an impact on human life. In addition to it, detecting fake currency is very challenging for the human eye. Detection of such currency is an important task and also consumes a lot of time if done manually. To address this critical issue, our paper contributes to identifying fake currency using Machine learning (ML) algorithms. In our article, we experimented with multiple ML algorithms to detect fake currencies and analyze their performance. Among the six algorithms experimented with for our work, Random Forest has shown the highest accuracy with 99.27% and a precision of 0.99 compared to the other algorithms. Additionally, we have demonstrated the interpretability of the ML model using LIME and SHAP which would be beneficial in understanding the important factors behind the particular prediction. We also performed hyperparameter tuning using GridSearchCV and manually too which showed us an improved accuracy performance of 99.99% from 99.27% for Random Forest. Feature Importance analysis and Explainability concepts are also demonstrated to provide an in-depth analysis of model prediction. 

Now a days due to the development in color printing technology the rate of counterfeit notes production and distribution is increasing. This is a massive problem, faced by almost all the countries. It affects the economy, sine it compromises the security of the real economy. Such counterfeit currencies are used to fuel nefarious motives, usually involving terrorist activities. According to the research, developing countries like India have been impacted by this very negatively. Even after the steps taken in 2016 to remove the counterfeits, by executing the demonetization of 500 and 1000 rupees bank notes in India the counterfeits of the new notes have begun circulating. This is due to the highly advanced technology adopted by the counterfeiters which makes the tracking of these counterfeit notes hard. This has become a very critical issue and the negative impact due to the counterfeit currency keeps rising. The only one solution for this problem for a common man is to detect the fake currency, by using the fake currency detector machine. These machines are used in banks and large scale business, but for a small business or for a common man these machines are not affordable. This paper gives the complete methodology of fake note detector machine, which is affordable even for a common man. By implementing the applications of image processing techniques we can find out whether the currency notes are fake or not. Image processing technique consists of a number of operations that can be performed on an image, some of which include image segmentation, edge detection, gray scale conversion etc. The proposed system will have advantages like simplicity, reliability and costs less.

Reenaz

Papers

Efficient Image Processing Technique for Authentication of Indian Paper Currency