Counterfeit Banknotes Research Articles

The impact of methods and means of combating counterfeit banknotes on economic security

The financial system plays a key role in economic development, ensuring high living standards and stable business activity. Money contributes to economic growth, but, at the same time, it also stimulates illegal actions aimed at their appropriation. The cases of counterfeiting that appeared with the minting of the first coins are gradually evolving due to technological innovations. Modern criminals use advanced methods of counterfeiting money, which presents significant difficulties even for the most advanced detection systems. Scammers focus on popular currencies such as the ruble, the US dollar and the euro, which leads to large-scale criminal activity, reaching the global level. Despite the efforts of law enforcement agencies and banks, criminal elements continue to find new ways to deceive, circumvent protective measures, and create difficult-to-recognize fakes, thereby posing threats to the country's economic security. To protect the monetary system of the state, banks and other organizations began to implement multi-level measures to counter criminal activity. Therefore, the article analyzes the impact of methods and means of combating counterfeit banknotes on economic security in the modern world.

Counterfeit banknote identification based on outliers detection methods

Counterfeit banknotes in circulation in the economy are a long-standing and recurring problem. Technological improvements are applied to produce increasingly convincing counterfeit banknotes. In contrast, sophisticated strategies to identify counterfeit notes remain an up-to-date and fascinating research topic of great interest. The literature presents several techniques for detecting counterfeit banknotes. In this study, outlier detection methodologies are presented as adapted mechanisms to assist in the fight against counterfeit banknotes. This study addresses and tests multivariate outlier detection techniques' efficiency in identifying counterfeit banknotes. A comprehensive computational experiment proves the great adaptability of the methods to multivariate outliers in the identification of counterfeit banknotes. In particular, the Data-driven Cluster Analysis Method (DDCAM) technique presented promising and optimistic results through several quality and performance measurement metrics, suggesting a hopeful future for detecting counterfeit banknotes.

Counterfeit Currency Detection Using Machine Learning

Counterfeit currency detection is a critical aspect of maintaining financial integrity. This paper introduces an innovative methodology for identifying counterfeit Indian rupee notes. By employing advanced image processing techniques and Convolutional Neural Networks (CNNs), specifically ResNet architecture, the proposed system achieves impressive accuracy in distinguishing genuine from counterfeit banknotes. The dataset, meticulously compiled through a combination of web scraping and synthetic printing, ensures robust training and evaluation. Experimental findings underscore the efficacy of the ResNet-based approach, highlighting its superiority over traditional CNN models. This research contributes significantly to bolstering currency security and trust in financial transactions, offering practical insights for combating counterfeit currency challenges.

Fake Currency Detection Using Machine Learning

The proliferation of counterfeit currency poses a significant threat to the global economy, necessitating the development of efficient and accurate detection methods. This paper presents a novel approach to fake currency detection using machine learning techniques. The proposed system leverages a combination of computer vision and machine learning algorithms to identify counterfeit banknotes with high accuracy. The system employs a convolutional neural network (CNN) to extract features from images of banknotes, which are then classified as genuine or fake using a support vector machine (SVM) classifier. The CNN is trained on a large dataset of images of genuine and fake banknotes, while the SVM is fine-tuned to optimize its performance. The Experimental results demonstrate that our proposed system achieves an accuracy of 98.5% in detecting fake currency, outperforming traditional methods based on manual inspection and rule-based systems. The system's performance has been evaluated using a comprehensive set of metrics, including precision, recall, F1-score, and receiver operating characteristic (ROC) curve analysis. The proposed system offers several advantages, including high accuracy, speed, and scalability, making it suitable for real-world applications in banking, finance, and law enforcement. Furthermore, the system's modular design enables easy integration with existing currency processing systems, facilitating seamless deployment. This research contributes to the development of intelligent systems for fake currency detection, providing a robust and reliable solution to combat counterfeiting.

Classification & Detection of Banknotes Using Machine Learning

Abstract: The only valuable resource in our country is bank currency, which is why money launderers inject fake notes that mimic genuine notes into the financial system to create imbalances. When the market is demonetized, there is a discernible quantity of counterfeit money floating about. In most cases, a human cannot easily tell the difference between a genuine note and a counterfeit one, even when many of the features of a counterfeit note are the same. Automated systems must beaccessible at banks or ATMs in order to identify bank currency authenticity. Since counterfeit banknotes are meticulously crafted, an effective algorithm that can determine whether a note is real or false must be createdin order to create such an automated system. In this study,we use a dataset from the UCI machine learning repository to apply the CNN algorithm. In order to put this into practice, we used machine learning algorithms and evaluated how well they performed using a range of quantitative analytical metrics.

Kejahatan Pengedaran Uang Kertas Palsu menurut Pasal 245 KUHP

The circulation of counterfeit money is not a simple, independent event, but is the result of various factors which coincidentally become a phenomenon that is very detrimental to all parties involved and also detrimental to the country in improving development. The circulation of counterfeit money is not a simple, independent event, but is the result of various factors which coincidentally become a phenomenon that is very detrimental to all parties involved and also detrimental to the country in improving development. In connection with the above, Law no. 1 of 1946 Articles 9 and 10 and also uses the mandate of the Criminal Code, especially Article 245 regarding the circulation of counterfeit banknotes in Indonesia. The basis for issuing the law is because money is a legal means of payment and is issued by Bank Indonesia and money as a means of exchange that has a fixed price can help a person fulfill various needs and achieve life goals that are considered important. However, in practice, the circulation of counterfeit money increases every year.

Jordanian banknote data recognition: A CNN-based approach with attention mechanism

Identifying counterfeit banknotes is crucial in financial transactions, as the process of identification cannot be handled by ATMs or vending machines. The recent developments in technology, particularly the smart systems that are integrated with cameras and artificial intelligence (AI) tools, allow for the distinction of currency and the detection of counterfeit. In this study, we are suggesting an approach for identifying counterfeit Jordanian banknotes and differentiating them from genuine ones. The suggested approach collaborates deep learning through a convolutional neural network (CNN) and another attention mechanism which contributes to focusing on features of importance while avoiding features of less importance. The proposed model has proven its ability to recognize counterfeits with high performance and accuracy while focusing on the important features extracted. The study made use of a data set from Kaggle that includes a collection of Jordanian banknotes in five different denominations. Image processing techniques were employed to produce artificial images by boosting the brightness of real ones. Eight trained models and the suggested model were compared. It demonstrated excellence with encouraging outcomes, achieving 96% accuracy, 96.6% precision, 96.4% recall, and 94.5% f1-score. Also, we tested our approach on two datasets Indian dataset and DS1, DS2, and DS3 datasets, we obtained 88% and 99.9% accuracy, respectively. The achievement of detecting counterfeit Jordanian banknotes is proof that a well-established AI model contributes to dealing with security vulnerabilities in many institutions.

Enhanced Counterfeit Detection of Bangladesh Currency through Convolutional Neural Networks: A Deep Learning Approach

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.

Подделка денежных знаков Банка России как угроза экономической безопасности России

The article analyzes the counterfeit banknotes number growth rate, the monetary policy aimed at improving the protective elements of banknotes, the level of counterfeiting crimes detection, and the impact of counterfeiting on the Russian economic security. The purpose of the article is to analyze the growth of cash circulation level and available methods for detecting counterfeit banknotes, as well as to determine the most relevant protective elements. The author uses the statistical data of the Central Bank of Russia on the amount of cash in circulation and counterfeit banknotes, as well as statistical data of the Ministry of Internal Affairs of Russia on the number of crimes committed under the Article 186 of the Criminal Code of the Russian Federation. The research can have a positive impact on the functioning of cash in general and become a basis for creating a unified information platform for the recognition of counterfeit banknotes, which will have a positive impact on the fight against counterfeiting and ensuring Russia’s economic security.

Several approaches of making photonic crystal anti-counterfeiting devices for banknotes

The popularity of counterfeit banknotes in the market will cause serious inflation. Under such circumstances, prices are soaring and paper money is seriously devalued, causing harm to people's livelihood. In recent years, the global circulation tendency offake banknotes has become larger, and counterfeit banknotes have become more and more difficult to distinguish, so several advanced photonic crystal anti-counterfeiting technologies emerged. This review will introduce the working principles of colloidal PC patterns and approaches of making photonic crystal anti-counterfeit banknotes. Then analyze the advantages and shortcomings of colloidal PC patterns.Finally ,make a conclusion to discuss the development prospects and possible challenges they may face.

TYPES OF RELIEF ELEMENTS ON BANKNOTES AND WAYS TO IMITATE THEM

The article is devoted to the consideration of methods for imitation of relief elements and perforation in the manufacture of counterfeit banknotes. In the manufacture of banknotes in modern conditions, the latest scientific and technical achievements are used. The reliability of protection of banknotes from forgery is ensured by the simultaneous use of a complex of security features of different levels. With the development of technological progress, new protective elements appear, and the reproduction of those already introduced into production gradually becomes available for reproduction by counterfeiters. In an effort to bring the appearance of counterfeit banknotes closer to those issued by issuing institutions, criminals use various techniques to imitate security elements and materials. Most often, they try to imitate the so-called public elements, known to most citizens and perceived by sight or by touch. A variety of relief elements are an indispensable attribute of the design of any banknote. Relief elements are among the important public security elements of banknotes issued for cash circulation. They are informative and easily perceived visually and tactilely. In addition to the protective function, they provide convenience for people with visual impairments to recognize the denomination of a banknote. At the same time, even a primitive forgery of the relief element of a banknote can mislead an ordinary citizen, because it imitates the presence of a corresponding sign or image. The article describes the technology of applying embossed signs, the concepts of «painted» and «unpainted» embossed signs, and elements with KIPP-effect or PEAK-effect, as well as these concepts are illustrated with examples using banknotes that are issued by issuing institutions. The methods of imitation of relief signs on counterfeit banknotes are highlighted and illustrated by cases from expert practice, namely: by making special relief forms and applying colourless embossing in the areas of the elements corresponding to the elements applied by intaglio printing on banknotes; imitation of relief elements on counterfeit banknotes, implemented by applying to the areas where relief elements should be located on a real banknote, convex strokes of arbitrary shape, or strokes formed by individual dots, which may not match the coloured image in content; creating relief by applying a layer of a transparent substance to the location of the corresponding inscription or image, made by inkjet or another non-relief printing method; by «powdering» a non-gravure print of a wet ink image with polymer powder on the face of the note to create a relief effect. The article gives a brief definition of micro-perforation, describes the technology of applying micro-perforation on banknotes, and provides illustrations of banknotes with elements applied by micro-perforation. Using examples from expert practice, methods for simulating perforation on counterfeit banknotes are considered, illustrating the features of the corresponding imitation method.

Counterfeit Detection using Deep Learning

Detecting counterfeit currency is a critical issue that has gained increasing attention in recent years. Deep learning techniques have shown significant promise in a variety of image processing tasks, including counterfeit currency detection. In this project, we propose a deep learning-based counterfeit currency detection system that uses convolutional neural networks (CNNs) for feature extraction and classification. The proposed system consists of two main phases: training and testing. In the training phase, a dataset of genuine and counterfeit banknotes is used to train the CNN model to distinguish between genuine and counterfeit banknotes. A CNN model extracts features from an input image using convolutional layers and then applies fully connected layers for classification. Experimental results show that the proposed deep learning-based counterfeit currency detection system achieves high accuracy. The system outperforms existing state-of-the-art techniques in terms of detection accuracy, robustness, and real-time performance. In conclusion, it can be said that the proposed system could be a useful tool for preventing the circulation of counterfeit currency and minimizing economic losses. The system can be integrated into various automated teller machines (ATMs) and vending machines to detect counterfeit money in real time

Authentication of Counterfeit Hundred Ringgit Malaysian Banknotes Using Fuzzy Graph Method

A banknote is a currency issued by a country, and it was first introduced in the 16th century. The counterfeiting of banknotes by cunning criminals became a great challenge with the current advanced technology. Forensic scientists are using chemical methods, such as Fourier transform infrared (FTIR) spectroscopy for differentiating genuine and counterfeit banknotes. However, the FTIR spectra of banknotes may require further pattern recognition analysis due to their high similarities. In this paper, a fuzzy graph-based algorithm for authentication of the FTIR spectrum, namely chemometrics fuzzy autocatalytic set (c-FACS), is used to discriminate between genuine and counterfeit hundred Ringgit Malaysian (RM100) banknotes. The results show that the genuine and counterfeit RM100 banknotes have slightly distinct patterns when analyzed using c-FACS. In addition, the results are compared with RM50 banknotes, and the results reveal that the nodes or dominant axis varies between the two banknotes. To verify the reliability of the results, the results obtained via c-FACS are compared with principal component analysis (PCA). The c-FACS showed better performances as compared to PCA in terms of time consumption and observation. Thus, the c-FACS has the ability to assist forensic investigations involving banknote counterfeiting crimes.

«Денежные воры» в России: об истории развития фальшивомонетничества в Курской губернии во второй половине XIX – начале XX века. По архивным материалам

The study and analysis of archival documents allows the authors to conclude that in the Kursk gubernia of the second half of the 19th – early 20th century there acted solitary criminals, producing and distributing counterfeit money independently, as well as criminal groups. Source base of the study is materials of the State Archive of the Kursk Region and those of the State Archive of the Russian Federation. The following methods have been used in the research: chronological, comparative-legal, method of synthesis, analysis and systematization. The study is to determine the specifics of the development of counterfeiting in the Kursk gubernia in the second half of the 19th - early 20th century. The novelty of the study is due to introduction of previously unused archival materials and use of comprehensive approach. The first counterfeiters appeared in the Russian state simultaneously with single monetary system formation. As this socially dangerous phenomenon developed, a mechanism was created to counteract the counterfeiting trade. It included legal and organizational measures. In the Council Code (Sobornoye Ulozheniye) of 1649 there was a chapter devoted entirely to protection of coins from forgery. Counterfeiting in Russia gained momentum in the second half of the 19th – early 20th century in the heyday of professional crime, when a tight-knit and well-consolidated “caste” of professional criminals was formed, including criminal community of professional counterfeiters. In the Code of Criminal and Correctional Punishments of 1845, forgery of a “exterior minted” coin was cited, tools and methods of counterfeiting banknotes were detailed. The Criminal Code of 1903 prohibited manufacture and sale of counterfeit money on professional basis (as a livelihood). Legal measures against counterfeiting were supplemented by organizational ones, such as creation of the Special Archive of Certificates on Cases of Manufacture and Sale of Counterfeit Money at the St. Petersburg District Court, and by educational work among the population on ways to recognize genuine money. However, counterfeiting was developing not only in major cities of the empire, but also in the Russian provinces, as numerous archival data proves. Counterfeit banknotes, as a rule, were passed off in urban areas (for example, in Oboyan, Kursk), which allowed criminals to evade criminal prosecution for a long time. Both paper money and coins were subject to falsification. They were produced by drawing, printing, coinage copying, casting, changing the ratio of precious and base metals in coin alloy. Special devices were often used to manufacture counterfeit money. However, the counterfeiters were not professionals of their craft, and thus counterfeit money were easily detected and recognized by law enforcement officers and population.

Detection of counterfeit banknotes using genetic fuzzy system

Detection of counterfeit banknotes using genetic fuzzy system

Automated detection and classification of counterfeit banknotes using quantitative features captured by spectral-domain optical coherence tomography

Counterfeiting of banknotes is still a severe crime problem in many countries. One of the most significant issue for solving the crime is to classify the counterfeit types and identify the sources. Most of the current methods to classify counterfeit banknotes rely on manual examination that is time-consuming and labor-intensive. Moreover, these methods only detect surface features which can be easily imitated through advanced printing technology. In this study, an automated method based on optical coherence tomography (OCT) and machine-learning algorithms was proposed to classify different types of banknotes based on the internal features. A spectral-domain OCT (SD-OCT) system was employed for sub-surface imaging and quantitative assessment of banknotes. A total of 29 Chinese 100-Yuan banknotes were collected, in which 4 of them were real and 25 of them were counterfeiting by three different printing processes. Each banknote was imaged 10 times in 3 distinct regions, which resulted in a dataset of 290 samples. Each sample was characterized by extracting 2 A-scan (OCT signal intensity along depth) based features and 14B-scan (cross-sectional OCT images) based features. Several machine-learning models, including logistic regression (LR), support vector machines (SVM), K-nearest neighbor (KNN) and random forest (RF), were built and optimized as the classifiers that were trained using 203 samples and applied to predict 87 testing samples. The best performance was achieved by SVM classifier in which the sensitivity of 96.55% and specificity of 98.85% were obtained in discriminating between authentic and counterfeit banknotes, and the sensitivity of 94.67% and specificity of 98.22% were obtained in predicting the types of counterfeit banknotes. These classifiers were also evaluated using the receiver operating characteristic (ROC) curves. To the best of our knowledge, this is the first study where A-scan and B-scan derived features from OCT images have been used for the detection and classification of different types of counterfeit banknotes.

TYPES OF WATERMARKS ON BANKNOTES AND METHODS OF THEIR IMITATION

The article deals with the consideration of ways to imitate watermarks in the manufacture of counterfeit banknotes. The watermark is an important public security element for banknotes issued for cash circulation. The basis of any banknote related to paper money is banknote paper. The article briefly describes the stages of the technological process of manufacturing banknote paper and methods for forming watermarks on it. The criteria for the classification of watermarks depending on their location on the banknote (general, local stripe), in relation to the overall tone of the paper (one-tone, two-tone, local, filigree, combined), composition, as well as drawings illustrating these types of watermarks are given. signs. The watermarks developed using modern technologies in recent decades are described, as well as their illustrations. The levels of counterfeiting are determined depending on the subject and its technical capabilities for the manufacture of counterfeit banknotes. The ways of imitation of watermarks are generalized. Firstly, watermarked paper intended for other purposes can be used to make counterfeit banknotes. Secondly, the imitation of watermarks is possible using specially made forms: embossed, used for embossing paper, and printed-screen or letterpress. Widely used to imitate watermarks of copiers. Less common are watermarks, which are imitated using fat-containing substances, or painted with a dye. The article provides examples of the implementation of the relevant methods with their illustration based on studies of counterfeit banknotes. Key words: banknote, banknote paper, watermark, types of watermarks, watermark imitation.

Detecting counterfeit banknotes with machine learning


 
 
 The one important asset of our country is Bank currency and to create discrepancies of money miscreants introduce the fake notes which resembles to original note in thefinancial market. During demonetization time it is seen that so much of fake currency is floating in market. In general, by a human being, it is very difficult to identify forged note from the genuine not instead of various parameters designed for identification as many features of forged note are similar to original one. To discriminate between fake bank currency and original note is a challenging task. So, there must be an automated system that will be available in banks or in ATM machines. To design such an automated system there is need to design an efficient algorithm which is able to predict weather the banknote is genuine or forged bank currency as fake notes are designed with high precision. In this project six supervised machine learning algorithms are applied on dataset available on UCI machine learning repository for detection of Bank currency authentication
 
 

Structural and optical characterization of banknotes using spectral-domain optical coherence tomography.

Counterfeiting of banknotes remains a severe threat to economic security and social stability. The characterization of banknote has mainly relied on the assessment of various security features applied to the surface of the note. However, the surface features are easy to forge and contain insufficient information to discover the source. In this paper, a novel approach for banknote characterization has been proposed by employing spectral-domain optical coherence tomography (SD-OCT) that can provide structural and optical features. Three groups of counterfeit Chinese 100 Yuan banknotes produced by different printing manners and one group of authentic banknotes were examined by SD-OCT without any sample preparation and four distinct areas were selected for imaging. High-resolution tomographic and three-dimensional (3D) volumetric OCT images were obtained and a set of features were first revealed to characterize the banknotes qualitatively and quantitatively. The results demonstrated that SD-OCT was effective to detect and classify different types of counterfeit banknotes and could potentially be used to link counterfeit banknotes to their sources in a fast, simple and nondestructive manner.

Counterfeit fifty Ringgit Malaysian banknotes authentication using novel graph-based chemometrics method

Counterfeiting, in particular, the forgery of banknotes is a serious crime problem and has become a great challenge to the global economies. The forensic science experts have been utilizing chemical technique such as infrared spectroscopy to analyze genuine and counterfeit banknotes. Nevertheless, chemometrics techniques are required to further discriminate the banknotes. In this paper, an advanced fuzzy graph chemometrics method is used to discriminate genuine and counterfeit fifty Ringgit Malaysian (RM50) banknotes. The development of the technique, namely chemometrics fuzzy autocatalytic set (c-FACS) is presented in this paper, together with the results and its comparison to principal component analysis (PCA) method. The results from the c-FACS analysis showed distinct patterns and features of the counterfeit banknotes in the c-FACS plot. Furthermore, the new method is faster than PCA in authentication analysis of counterfeit banknotes. Hence, the c-FACS provides better performance in terms of computing time as compared to PCA, and has the potential in assisting the investigation involving counterfeit banknotes.