Fake Currency Detection using Machine Learning Algorithm

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 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.

International Journal of Engineering and Advanced Technology, 2019

Great technological advancement in printing and scanning industry made counterfeiting problem to grow more vigorously. As a result, counterfeit currency affects the economy and reduces the value of original money. Thus it is most needed to detect the fake currency. Most of the former methods are based on hardware and image processing techniques. Finding counterfeit currencies with these methods is less efficient and time consuming. To overcome the above problem, we have proposed the detection of counterfeit currency using a deep convolution neural network. Our work identifies the fake currency by examining the currency images. The transfer learned convolutional neural network is trained with two thousand, five hundred, two hundred and fifty Indian currency note data sets to learn the feature map of the currencies. Once the feature map is learnt the network is ready for identifying the fake currency in real time. The proposed approach efficiently identifies the forgery currencies of ...

IRJET, 2020

The main purpose of this project is to obtain a false-positive income using Machine Learning. This process can be automated on mobile using the application software. Basic logic is developed using image acquisition, image segmentation, feature extraction and comparison. Enlarged images of the real currency are transferred to the Machine learning dataset. The features of the note to be tested are compared to a dataset made from an actual enlarged image and determine whether it is real money or fake. The most important challenge is to repeat the systematic and systematic review process to reduce error and time. In recent years, a large number of counterfeit coins have been printed & at the same time other illegal rings are producing and selling counterfeit coins, resulting in massive loss and damage to society. So, it is a color to be able to get fake money. We propose a new proposal for obtaining fake Indian notes using their images. A coin image is represented in a space of differences, which is a vector space created by comparing an image with a set of real money proteins. Each scale measures the differences between the image presented and the model presented. In order to find the differences between the two images, the key points of the location of each image are identified and explained. Based on the characteristics of the currency, the corresponding key points between the two images can be clearly identified. The posting process is also proposed to remove key points. Due to the limited number of non-real-world funds, SVM is designed to detect fake cash, so only real money is needed to train a classifier.

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.

Zenodo (CERN European Organization for Nuclear Research), 2023

The printing and scanning industry has made significant technological advancements, but unfortunately, this has also led to a rise in counterfeiting. Counterfeit currency can negatively impact the economy and reduce the value of genuine money. Therefore, detecting fake currency is crucial. Traditional methods have relied on hardware and image processing techniques, which can be inefficient and timeconsuming. To address this issue, we have proposed a new approach that uses a deep convolutional neural network to detect counterfeit currency. Our method analyzes currency images and can efficiently identify fake currency in real time. We trained a transfer learned convolutional neural network using a dataset of two thousand currency notes to learn the feature map of genuine currency. Once the feature map is learned, the network is able to identify counterfeit currency quickly and accurately. Our proposed approach is highly effective and significantly reduces the time required to identify fake currency among the 500 notes in our dataset. I.

International Journal of Advanced Research in Science, Communication and Technology

In this method, the Automatic fake Currency Recognition System is used to identify fake paper money and determine if it is genuine or not. The current counterfeit issue brought on by demonetization has an impact on the financial system as well as other sectors. This strategy, which is comparatively superior to earlier image processing methods, examines a novel Convolution Neural Network approach for the identification of fake money notes through their images. This approach is based on Deep Learning, which has recently shown outstanding results in image categorization problems. Through the use of an image of the fake money note, this approach can assist both humans and machines in instantly recognizing the note. In this system original and fake notes images are used to perform training and classification operation. The proposed system achieved an accuracy of 99.46% and loss of 0.0033 using CNN algorithm.

IFIP Advances in Information and Communication Technology, 2013

Counterfeit currency varies from low quality color scanner/printer-based notes to high quality counterfeits whose production is sponsored by hostile states. Due to their harmful effect on the economy, detecting counterfeit currency notes is a task of national importance. However, automated approaches for counterfeit currency detection are effective only for low quality counterfeits; manual examination is required to detect high quality counterfeits. Furthermore, no automatic method exists for the more complex-and important-problem of identifying the source of counterfeit notes. This paper describes an efficient automatic framework for detecting counterfeit currency notes. Also, it presents a classification framework for linking genuine notes to their source printing presses. Experimental results demonstrate that the detection and classification frameworks have a high degree of accuracy. Moreover, the approach can be used to link high quality fake Indian currency notes to their unauthorized sources.

International Journal of Computer Graphics, 2020

We propose a technique for web access by infusing or embeddings ordering different nations notes. An Image is separating and preparing procedure to recognize and match the distinguished information required cash picture and the first reference picture, each money note taken a Region of Interest (ROI) on existing money note condition. A separated cash picture ROI can be utilized to different example development and acknowledgement procedures and ANN hubs recognizing systems. At once, numerous cash notes are distinguished by coordinated notes then a web seek based following framework to recognize coordinating procedure is allowed for getting to for their specified timeframe. At first, we secure required the cash note by average level picture scanner on settled dpi shading with a required size arrangement; the dpi pixels level is set to get an ordinary picture utilizing picture preparing strategy. Barely any cutting edge picture channels are connected to proposed picture remarkable estimation of required cash take note of, this relegated esteem or images are contrasted and the doled out info sign images to coordinate unique note esteem, at that point web-based getting to technique controls by the microcontroller to examine all prerequisite fields and fundamental activities. 1

International Journal of Computer Science and Mobile Computing

There are persistent rumors about counterfeit money all across the world. There is a massive loop of producing counterfeit currency that is developing alongside technology. Counterfeit currency production has gotten easier and more advanced day by day, so the detection process has gotten more challenging. Utilizing a variety of user-friendly counterfeit detection tools or software is the only method to stop fraud. Many people still do not have access to these softwares or tools for detecting fakes. Therefore, some of these programs or tools are not accurate, dependable, and free. This paper describes a potential software solution for detecting fake Bangladeshi banknotes. The most important thing is that it is totally free of cost and usable by all average people. The primary purpose of this software is to identify different currencies and determine whether it is real or fake. In this paper, Convolutional Neural Network (CNN) and FLANN-based Matcher with the Scale-Invariant Feature T...

IJCSMC, 2019

Fake Currency has always been an issue which has created a lot of problems in the market. The increasing technological advancements have made the possibility for creating more counterfeit currency which are circulated in the market which reduces the overall economy of the country. There are machines present at banks and other commercial areas to check the authenticity of the currencies. But a common man does not have access to such systems and hence a need for a software to detect fake currency arises, which can be used by common people. This proposed system uses Image Processing to detect whether the currency is genuine or counterfeit. The system is designed completely using Python programming language. It consists of the steps such as grayscale conversion, edge detection, segmentation, etc. which are performed using suitable methods.

IRJET, 2022

The global economy is vulnerable to counterfeit currency. Advanced printing and scanning technologies have made it a common occurrence. For both people and corporations, fake currency recognition is a serious issue. The creation of counterfeit banknotes, which are barely distinguishable from legitimate currency, is a continuous process for counterfeiters. To detect fake notes, several traditional techniques and approaches are available based on colors, widths, and serial numbers. This paper discusses different methods of fake currency detection using image processing.

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 .

The advancement of color printing technology has increased the rate of fake currency note printing and duplicating the notes on a very large scale. Few years back, the printing could be done in a print house, but now anyone can print a currency note with maximum accuracy using a simple laser printer. As a result the issue of fake notes instead of the genuine ones has been increased very largely. India has been unfortunately cursed with the problems like corruption and black money .And counterfeit of currency notes is also a big problem to it. This leads to design of a system that detects the fake currency note in a less time and in a more efficient manner. The proposed system gives an approach to verify the Indian currency notes. Verification of currency note is done by the concepts of image processing. This article describes extraction of various features of Indian currency notes. MATLAB software is used to extract the features of the note. The proposed system has got advantages like simplicity and high performance speed. The result will predict whether the currency note is fake or not.

2020

1PG Student, Dept. Of Computer Science and Engineering, Lingaya’s Vidyapeeth, Faridabad, India 2Assistant Professor, Dept. Of Computer Science and Engineering, Lingaya’s Vidyapeeth, Faridabad, India ---------------------------------------------------------------------***---------------------------------------------------------------------Abstract This paper developed a computer vision based approach for Indian paper currency detection. In this approach, extract currency feature and develop an own datasets used for the currency detection. By using feature extraction method of front and back side Rs. 200 denomination security feature of Indian currency note. The mainly use ORB (Oriented FAST and Rotated BRIEF) and Brute-Force matcher approach to extract the feature of paper currency, so that can more accurately detection the denomination of the banknote both obverse and reverse. Our main contribution is through using ORB and BF matcher in OpenCV based, the average accuracy of detectio...