Detection of Fake Currency Using KNN Algorithm

Zenodo (CERN European Organization for Nuclear Research), 2023

A huge deal of counterfeit currency has been printed recently, which has hurt society greatly. Therefore, the creation of a method to identify fraudulent currency has become essential. By using their image, our proposed system will employ a method to identify counterfeit cash notes traded in our nation. Our work will offer the necessary adaptability and compatibility for the majority of individuals, as well as dependable accuracy for the detection of counterfeit currencies. To make this application effective, we are employing the logistic regression algorithm. Using a machine learning algorithm, this work will identify several significant properties in notes that will establish the currency note's uniqueness. This application makes it simple to spot fraudulent notes and reduces their availability on the market.

2021

Malpractising has always been a serious challenge that resulted in a serious problem in society. The automation in technology creates a more copied currency that is entirely spread, resulting in reducing the economic growth of the country. The note detection is compulsory, and also necessary to be very consistent and reliable. The paper currency identification depends upon a number of steps, including edge detection, feature extraction, image segmentation, grayscale conversion, and comparison of images. This paper also consists of a literature survey consisting of different methodologies for detection. The review to detect malpractice concludes that whenever we apply some efficient preprocessing and feature extraction techniques, it helps in improving the algorithm as well as the detection system. Machine Learning techniques help in building tools that is required and necessary for the research work, and we can make computer learning design, implementation, and methods to have a dif...

Indonesian Journal of Electrical Engineering and Computer Science

Currency counterfeiting is a significant offense that has an impact on a nation's finances. Due to the enormous progress in printing technology, it is now quite simple to create fake currency that resembles real currency in both appearance and texture, making it nearly difficult to manually tell them apart. The suggested approach will be helpful in identifying fake currency in financial systems. Because of the rise of fake currency in the market, numerous false note detecting techniques are available globally to address this issue, however the most of them rely on expensive technology. In this paper, we'll introduce a revolutionary way for separating fake banknotes from real ones using the support vector machine (SVM) approach. To categorize bank notes as authentic or counterfeit utilizing the data retrieved from the photos of the bank notes, SVM performs better overall and is more effective, particularly when it comes to pattern categorization. Finally, the results of our e...

International Journal for Research in Applied Science and Engineering Technology, 2021

The Currency Recognition System was developed for the purpose of fraud detection in paper currency, so this system is u sed worldwide. The uses of this framework can be recognized in banking frameworks, cash observing gadgets, cash trade frameworks. This paper proposes an automatic paper currency recognition system through an application developed using Machine learning Algorithms. The algorithm implemented is simple, robust and efficient.

IRJET, 2020

This paper presents the various fake currency detection techniques. Fake currency is imitation currency produced without the legal sanction of the state or government. Production and printing of Fake notes of Rs.100, 200, 500 and 2000 are degrading economic growth of our country. From last few years due to technological advancement in color printing, duplicating, and scanning, counterfeiting problems are coming into picture. So, Fake currency detection system has become more and more important. In this paper verification of fake currency note is done by the concepts of image processing. MATLAB is used to extract the features of the real and fake notes. The comparison between the features will predict whether the currency note is fake or not.

International journal of engineering research and technology, 2017

In India,’ currency’ is the means of Transaction so there is more value for currency in our social and economic development. Here, currency exists in the form of coins, banknotes and electronic data. Fake money or counterfeit notes is the dangerous or acute problem in front of whole world, and India is also a part of this fake currency. Modernization in the financial system is a milestone in protecting the economic development and now a days Indian government has become conscious about this so demonetization of Rs 1000 and Rs 500 notes is the latest example of it. But again we have Rs 2000 as a new currency in market. so as the highest value note there is a chance that corrupt people will try to make it as a counterfeit. So main objective of this paper is to study different key features of new genuine currency and use such techniques to detect and verify new currency circulated by Reserve Bank Of India. There are Different techniques which are used to distinguish between counterfeit...

2014

In India Every year RBI (Reserve bank of India) face the problem on counterfeit currency notes.The bank staffs are specially trained to detect counterfeit notes but problem begins once such notes are mixed into the market and circulated through common people. Even receiving fake notes from ATM counters have also been reported at some places. Over the past few years, as a result of the great technology come advances in color printing, duplicating and scanning counterfeiting problems become increses. In the previous, only the printing house has the ability to make counterfeit paper currency, but today it is possible for any person to print counterfeit bank notes simply by using a computer and a laser printer at house. Therefore to stop these issue The Indian currency notes recognition system is very useful .In order to deal with such type of problems, an automated Recognition of currency notes is introduced with the help of feature Extraction, classification based in SVM, Neural Net.ANN is introduced to train the data and classify the segments Using its datasets. This technique is considered with the computer vision where all processing with the image is done by machine. The machine is fitted with a CDD camera which will scan the image of the currency note considering the dimensions of the banknote and software will process the image segments with the help of SVM and character recognition methods. To implement this design we are dealing with MATLAB Tool .

World Review of Entrepreneurship, Management and Sustainable Development, 2018

Counterfeiting is an exhaustive problem smashing extensively, virtually as well as in reality, on each sector all around the world. In order to identify and classify fake and genuine banknote various techniques and models have been proposed and developed. This paper proposes an effective predictive model based on machine learning technique for authentication of banknotes, which can predicts with good accuracy that whether the given banknote is fake or genuine. The decision tree model is built using IBM SPSS tool. The performance measure of the model is done using gain charts and index charts and it is found that proposed decision tree model is good enough for prediction of banknote classification as fake or genuine.

International Journal of Emerging Trends in Engineering Research, 2021

Fake Currency Detection is the biggest problem faced by many countries including India. The advancement of colour printing technology has increased the rate of fake currency note printing and duplicating the notes on a very large scale. So, it has become a necessity to develop a tool that detects the fake currency note in a less time and in a more efficient manner using Image processing.

2020

The production of counterfeit paper currencies has become cheaper because of the advancement in the printing technologies. The circulation of counterfeit currencies down the economy of a country. By leveraging this, there is a mandate to develop an intelligent technique for the detection and classification of counterfeit currencies. The intelligent techniques play a major role in the field of Human Computer Interaction (HCI) too. This paper deals with the detection of counterfeit Indian currencies. The proposed method feature extraction is based on the characteristics of Indian paper currencies. The first order and second order statistical features are extracted initially from the input. The effective feature vectors are given to the SVM classifier unit for classification. The proposed method produced classification accuracy of 95.8%. The experimental results are compared with state-of-the methods and produced reliable results.