Fake Currency Detection

Project Title

Fake Currency Detection

Project Area of Specialization Computer ScienceProject Summary

Counterfeit money devalues the currency it is meant to copy. Many economists believe that counterfeit money is one of the worst things that can happen to a country's economy, as it undermines trust in the currency's value. The US Treasury and the Federal Reserve have control over the money supply. And by forging the money, one is stealing the thunder of federal government. They don't take it lightly. A highresolution printer can now make a counterfeit note that looks very similar to the real one, thanks to advances in technology. The net outcome of counterfeit money is Inflation.Counterfeiting currencies has been easier since the introduction of highly advanced reprographic processes, such as laser printing. Clearly, counterfeit moneys identification is critical not just for business owners, but also for consumers.  This project proposes a software working on python and Deep Learning and using image processing technique to automatic detection and recognition of counterfeit currency.

Project Objectives

The project's main goal is to use a Machine Learning Algorithm to automatically identify fraudulent US dollar notes. Despite the fact that there were other techniques available, this method was created to address the shortcomings of the prior methods. Following are the objectives of this system.

1. To identify how people are counterfeiting the currency
2. To critically review the literature concerning counterfeiting the money 
3. To propose a model dynamically effective for distinguish between genuine and fake currency                                                                                                 

Project Implementation Method

METHODOLOGY

The acquisition of forged documents is required for many programs such as Banking, Selling Goods. Manual counting and fraudulent identification are time-consuming task. Therefore, the system of obtaining forged documents in US takes US counterfeit money according to certain security features with the help of a system that can determine whether the Banknote is genuine or not.

1. Using Machine Learning

All images are processed using Computer Viewing Techniques. Feature extraction is done using the cv2 module. The SVM algorithm is used for features, so the separation is done with Vector Machine support. After that, the CNN model is made. This uses several deeper layers to extract the features. The test image is selected, and the image elements are extracted in pixels. These selected image features are compared to the actual features of the note. If the analogy is shown as real money, if the opposite is also shown as fraudulent.

1. Using Deep Learning

In our practice we use Deep Learning models such as Deep Neural Network and Convolutional Neural Network (CNN). Deep learning methods are able to capture the complex relationship between millions of pixels. CNN is commonly used in computer viewing, but recently used in image segregation functions CNN is made up of two main components:

1. Feature Extraction: In this section, the network will perform a series of integration and integration operations where features are acquired. If you had a picture of a zebra, this is the part where the network could see its stripes, two ears and four legs
2. Classification: Here, the fully connected layers will act as a further separation of these extracted elements. They will allow the object in the image to be what the algorithm says.

Benefits of the Project

This project will help in identification of counterfeit currency the main idea of the project is based on key features of currency that are difficult to counterfeit                                                                          

The proposed system will declare result after processing the currency note and predict whether the currency is real or fake.

Technical Details of Final Deliverable

we will design a system that detects fake currency using pythn language with image proccessing techniques and it will help detect the fake notes 

Final Deliverable of the Project Software SystemCore Industry SecurityOther Industries Others Core Technology Artificial Intelligence(AI)Other Technologies OthersSustainable Development Goals Decent Work and Economic GrowthRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	70000
laptop	Equipment	1	60000	60000
DATASET	Equipment	1	10000	10000