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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.
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 ...
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
Counterfeiting poses a significant threat to the stability of Bangladesh's currency, the Taka, necessitating advanced methods for detection and prevention. This paper presents an innovative approach to counterfeit detection using Convolutional Neural Networks (CNNs), a deep learning technology. Explicitly focused on Bangladesh's currency, this method aims to enhance the accuracy and efficiency of counterfeit detection by leveraging the power of artificial intelligence. The proposed approach involves training CNNs on a dataset of authentic and counterfeit Bangladeshi currency images, allowing the network to learn intricate features and patterns indicative of counterfeit notes. By exploiting the hierarchical structure of CNNs, the system can automatically extract discriminative features from currency images, enabling robust detection of counterfeit banknotes. The CNN-based approach offers several advantages compared to traditional methods, which often rely on manual inspection or rule-based algorithms. It can handle complex visual information more accurately and efficiently, making it well-suited for detecting subtle counterfeit features.Furthermore, the adaptability of CNNs allows for continuous learning and improvement, ensuring resilience against evolving counterfeit techniques. The efficacy of the proposed method is validated through extensive experimentation and evaluation, demonstrating its superior performance in detecting counterfeit Bangladesh currency notes. By harnessing the capabilities of deep learning, this approach not only enhances the security of Bangladesh's financial system but also serves as a scalable solution applicable to other currencies and regions facing similar challenges. In conclusion, the integration of Convolutional Neural Networks represents a significant advancement in counterfeit detection technology, offering a powerful and versatile tool for safeguarding the integrity of Bangladesh's currency and combating financial fraud on a global scale.
Recently, analyzing multiple types of fake banknote recognition and detection is a key concern in finance and business. Fake detection is an increasing methodological approach with the significance and technologies in an enormous amount of banknote image data with high dimensionality and unprecedented speed, which leaves a massive data gold ore waiting to be mined. Therefore, in this paper, we proposed a deep CNN technique to differentiate between real and fake banknotes using the fake detection method by examining the computer vision features of the digital content for detecting fake banknotes using smartphone cameras in a cross-dataset environment. The proposed CNN model is used to classify and detect real and fake banknotes datasets for Ethiopian banknotes confirming that the proposed algorithm demonstrates a higher detection accuracy. The detection model sequence includes image acquisition, Image size normalization, grayscale conversation, and histogram equalization, which suppo...
Proceedings of the 5th edition of the Computer Science Research Days, JRI 2022, 24-26 November 2022, Ouagadougou, Burkina Faso
In West Africa, counterfeit CFA banknotes impact the economic growth of states. Because of this scourge, we are witnessing a decline in the purchasing power of the population. However, some hardware kits for detecting counterfeit CFA banknotes are available on the market. These kits are expensive for the actors of the informal sector. For them, these kits are not easily portable and also frequently break down. In this work, we propose an approach based on deep learning for the detection of counterfeit CFA banknotes through an Android application. Furthermore, the proposed approach is a first one in the WAEMU in the field of research for the CFA banknote forgery detection. We use an image dataset of over 4000 genuine and counterfeit ten-thousand CFA banknote for our model training. The images of the banknotes are taken by a smartphone camera. We use the convolutional neural network Alexnet for banknote classification. The accuracy of the training model reaches 99.7% for the detection of counterfeit CFA banknotes.
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 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
2020 11th International Conference on Electrical and Computer Engineering (ICECE), 2020
Automatic detection and recognition of banknotes can be a very useful technology for people with visual difficulties and also for the banks itself by providing efficient management for handling different paper currencies. Lightweight models can easily be integrated into any handy IoT based gadgets/devices. This article presents our experiments on several state-of-the-art deep learning methods based on Lightweight Convolutional Neural Network architectures combining with transfer learning. ResNet152v2, MobileNet, and NASNetMobile were used as the base models with two different datasets containing Bangladeshi banknote images. The Bangla Currency dataset has 8000 Bangladeshi banknote images where the Bangla Money dataset consists of 1970 images. The performances of the models were measured using both the datasets and the combination of the two datasets. In order to achieve maximum efficiency, we used various augmentations, hyperparameter tuning, and optimizations techniques. We have ac...
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.
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 .
Coins recognition systems have humungous applications from vending and slot machines to banking and management firms which directly translate to a high volume of research regarding the development of methods for such classification. In recent years, academic research has shifted towards a computer vision approach for sorting coins due to the advancement in the field of deep learning. However, most of the documented work utilizes what is known as ‘Transfer Learning’ in which we reuse a pre-trained model of a fixed architecture as a starting point for our training. While such an approach saves us a lot of time and effort, the generic nature of the pre-trained model can often become a bottleneck for performance on a specialized problem such as coin classification. This study develops a convolutional neural network (CNN) model from scratch and tests it against a widely-used general-purpose architecture known as Googlenet. We have shown in this study by comparing the performance of our m...
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
The use of technology has grown tremendously within the few years it has made it easier to have access to advanced printing equipment in the industry which resulted in color printing of currencies to produce counterfeit notes across the country. To eliminate such unethical activities of printing counterfeit currency it is mandatory to make a system that detects the fake currency, In systems such as a money exchanger for example ATMs and vending machines, counterfeit currency notes must be detected beforehand exchanging process takes place. In the past, there have been similar systems developed based on methods such as image processing techniques that are done on the Matlab platform and other such platforms these methods possess some limitations including being less efficient and time-consuming. Our system is designed to eliminate all of the above problems through the use of deep learning techniques by detecting the features of currencies and determining whether its fake with a great accuracy rate. our proposed system verifies the Indian currency notes using Deep learning, deep learning helps in extracting meaningful information from the dataset fed into the machine using a set of methods to perform the classification of images. Our project makes use of the deep learning framework TensorFlow and its high-level API Keras which simplifies the creation of the model making it easier to achieve a less time-consuming and accurate model.
2018
The advance of scanner and printer technologies has increased the possibility of making counterfeit bills that cannot be distinguished by human and simple detecting devices. The rate of finding counterfeit bills by individuals is very low because counterfeit bill detectors require too high a cost. In this paper, we propose a deep learning-based algorithm to detect counterfeit bills through general-purpose scanners that can be used by individuals to prevent personal monetary damages caused by counterfeit bills. The proposed algorithm adopts a convolutional neural network model that consists of 2 convolutional layers and 2 fully connected layers. In convolutional layers, rectified linear unit and max-pooling are applied. In fully connected layers, drop out is applied. Using original bills and counterfeit bills printed by various manufacturers' printers, experiments are performed. Also, the proposed algorithm is compared with previous feature-based algorithms to show the outstandin...
IRJET, 2020
Right now, Fake Currency Acknowledgment System is intended to identify the fake paper cash to check whether it is phony or unique. The current fake issue due to demonetization impacts the financial framework and furthermore in other fields. Another methodology of Convolution Neural Network towards ID of phony money notes through their pictures is inspected right now is similarly better than past picture handling strategies. This technique is in light of Deep Learning, which has seen huge achievement in picture arrangement assignments lately. This method can support the two individuals and machine in recognizing a phony cash note continuously through a picture of the equivalent. The proposed framework can likewise be conveyed as an application in the cell phone which can push the general public to recognize the phony and unique cash notes. The Precision in the proposed framework can be expanded through the high number of unique and phony notes.
2018
Counterfeit bills are easy to forge due to the advances in scanning and printing technologies. Individuals are less likely to find counterfeit bills. This paper proposes a deep learning-based algorithm to detect counterfeit bills and their forgery devices. The proposed algorithm has adopted a convolutional neural network model composed of 2 convolutional layers and 2 fully connected layers. In the convolutional layers, rectified linear unit and max-pooling are applied. In the fully connected layers, drop out is applied. To show the performance of the algorithm, experiments are performed using original bills and counterfeit bills forged with different manufacturers' printers. Nearly 100% detection accuracy has been achieved. Keywords-counterfeit bill detection; forgery device detection; deep learning; convolutional neural network.
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...
PeerJ Computer Science
Conventional paper currency and modern electronic currency are two important modes of transactions. In several parts of the world, conventional methodology has clear precedence over its electronic counterpart. However, the identification of forged currency paper notes is now becoming an increasingly crucial problem because of the new and improved tactics employed by counterfeiters. In this paper, a machine assisted system—dubbed DeepMoney—is proposed which has been developed to discriminate fake notes from genuine ones. For this purpose, state-of-the-art models of machine learning called Generative Adversarial Networks (GANs) are employed. GANs use unsupervised learning to train a model that can then be used to perform supervised predictions. This flexibility provides the best of both worlds by allowing unlabelled data to be trained on whilst still making concrete predictions. This technique was applied to Pakistani banknotes. State-of-the-art image processing and feature recognitio...
Proceedings of the 2nd International Conference on Computing Advancements
As soon as coins or money was invented, there were people trying to make counterfeits. Counterfeit money is fake money that is produced without the permission of the state or government, usually to imitate the currency and deceive the intended recipient. In Bangladesh, this is a significant problem and the problem is becoming more and more phenomenon as the days are passing by. Today's modern bank notes have several security features that makes easier to identify fake notes. One of the security features is the use of UV ink. Bank notes deliberately put random flecks of color scattered all over the surface of the money-which acts as a extra layer of protection against counterfeiters. We propose an automatic authentication model for identifying counterfeit money based on these random flecks of color which is visible under UV light. To obtain a benchmark result, existing object detection pre-trained models were used, followed by MobileNet, Inception, ResNet50, ResNet101, and Inception-ResNet architectures. After that, using the Region Proposal Network (RPN) method with Convolutional Neural Network (CNN) based classification the optimal model was proposed. The proposed model had a 96.3 percent accuracy. It is critical to reduce the circulation of counterfeit money in a country's economy to stop inflation. This study will aid in the detection of counterfeit money and, hopefully, reduce its spread.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022
In recent years, deep learning has become the most popular research direction. It mainly trains the dataset through neural networks. There are many different models that can be used in this research project. Throughout these models, accuracy of currency recognition can be improved. Obviously, such research methods are in line with our expectations. In this paper, we mainly use transfer learning (MobileNet) model based on deep learning as the framework, Convolutional Neural Network (CNN) model to extract the features of paper currency, so that we can more accurately classify the currency. Our main contribution is through using CNN and MobileNet, the average accuracy of currency classification is up to 99%.
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.
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...
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