Search Results (20)

This article examines vulnerability to counterfeit currency fraud in Bulgaria by assessing citizens’ competence in recognizing genuine banknotes of the national currency (BGN) prior to the introduction of euro banknotes in 2026. Counterfeit banknotes represent a form of economic crime in which individual victims’ losses are closely tied to their ability to authenticate cash in everyday transactions. Drawing on level-1 security features and guidelines of the Bulgarian National Bank, we developed a structured questionnaire to operationalize knowledge of key authenticity checks (hologram, intaglio printing, watermark, security thread, see-through register). The survey was administered online and on paper over a 20-day period (22 August–11 September 2025) and completed by 371 respondents from across the country. Using descriptive statistics tools, we identify three distinct groups: (i) highly competent respondents who reliably distinguish genuine from counterfeit banknotes; (ii) individuals with high self-reported confidence but inconsistent performance; and (iii) a particularly vulnerable group with low knowledge of security features, limited awareness of official guidance and low self-confidence. Vulnerability is significantly associated with lower education, residence in smaller settlements, lack of prior exposure to counterfeit banknotes and absence of contact with institutional information campaigns. The findings have direct implications for crime prevention and criminal justice policy: they provide an evidence base for targeted public awareness initiatives and risk-based allocation of resources aimed at protecting high-risk groups from currency-related fraud in the context of the monetary transition. Full article

Background: This study presents a real-time mobile system for identifying mixed and partly covered foreign coins and banknotes using the You Only Look Once version 11 (YOLOv11) deep learning framework. The proposed system addresses practical challenges faced by travelers and visually impaired individuals when handling multiple currencies. Methods: The system introduces three novel aspects: (i) simultaneous recognition of both coins and banknotes from multiple currencies within a single image, even when items are overlapping or occluded; (ii) a hybrid inference strategy that integrates an embedded TensorFlow Lite (TFLite) model for on-device detection with an optional server-assisted mode for higher accuracy; and (iii) an integrated currency conversion module that provides real-time value translation based on current exchange rates. A purpose-build dataset containing 46 denominations classes across four major currencies: US Dollar (USD), Euro (EUR), Chinese Yuan (CNY), and Korean Won (KRW), was used for training, including challenging cases of overlap, folding, and partial coverage. Results: Experimental evaluation demonstrated robust performance under diverse real-world conditions. The system achieved high detection accuracy and low latency, confirming its suitability for practical deployment on consumer-grade smartphones. Conclusions: These findings confirm that the proposed approach achieves an effective balance between portability, robustness, and detection accuracy, making it a viable solution for real-time mixed currency recognition in everyday scenarios. Full article

Fire can be one of the most destructive elements to cause devastation. Fire can completely or partly destroy any crucial and invaluable documents, such as banknotes, books, affidavits, etc., in a couple of minutes. Moreover, the documents can also be damaged by heat, smoke, soot, and water during an accident. The burnt documents become fragile, losing their identity, which may have some evidentiary value related to the incident. Therefore, there is a strong need for processing to procure, preserve, and decipher, i.e., to restore the texts written on them. Hence, the present research focuses on developing a new method using natural polysaccharides, i.e., starch, to preserve and decipher the contents of charred documents. The most suitable concentration of starch analog was found to be 6% microwaved at 80 °C for about 10 min. As soon as the charred documents were coated with 6% starch analog, the majority of the invisible texts became visible to the naked eye in a second. Moreover, the application of a synthesized analog of polysaccharide on fragile charred documents provided an appreciable increase in strength by almost 0.1 kg/cm² for the coated charred documents of each paper type compared to that of non-coated ones and made them stabilized. This research also involves the use of easy and advanced handwriting recognition techniques (HCR) using an easily accessible, free platform, G-lens, that successfully recognized the majority of texts deciphered using 6% starch analog and converted them from captured images to a readable and copyable text format. Furthermore, the document visualization under VSC also gave a promising result by enhancing and deciphering the non-visible and less visible texts under flood light and white spot light at 715 and 695 long passes. Hence, this study offers an environmentally friendly, cost-effective, and sustainable approach of using a natural polysaccharide instead of synthetic polymers for the preservation and decipherment of charred documents. Full article

With the proliferation of smartphones and advancements in deep learning technologies, object recognition using built-in smartphone cameras has become possible. One application of this technology is to assist visually impaired individuals through the banknote detection of multiple national currencies. Previous studies have focused on single-national banknote detection; in contrast, this study addressed the practical need for the detection of banknotes of any nationality. To this end, we propose a multinational banknote detection model (MBDM) and a method for multinational banknote detection based on mosaic data augmentation. The effectiveness of the MBDM is demonstrated through evaluation on a Korean won (KRW) banknote and coin database built using a smartphone camera, a US dollar (USD) and Euro banknote database, and a Jordanian dinar (JOD) database that is an open database. The results show that the MBDM achieves an accuracy of 0.8396, a recall value of 0.9334, and an F1 score of 0.8840, outperforming state-of-the-art methods. Full article

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. Full article

The objective of this study was to establish an automated system for the recognition of banknote serial numbers by developing a deep learning (DL)-based optical character recognition framework. An integrated serial number recognition model for the banknotes of four countries (South Korea (KRW), the United States (USD), India (INR), and Japan (JPY)) was developed. One-channel image data obtained from banknote counters were used in this study. The dataset used for the multi-currency integrated serial number recognition contains about 150,000 images. The class imbalance problem and model accuracy were improved through data augmentation based on geometric transforms that consider the range of errors that occur when a bill is inserted into the counter. In addition, by fine-tuning the recognition network, it was confirmed that the performance was improved when the serial numbers of the banknotes of four countries were recognized instead of the serial number of a banknote from each country from a single-currency dataset, and the generalization performance was improved by training the model to recognize the diverse serial numbers of multiple currencies. Therefore, the proposed method shows that real-time processing of less than 30 ms per image and character recognition with 99.99% accuracy are possible, even though there is a tradeoff between inference speed and serial number recognition accuracy when data augmentation based on the characteristics of banknote counters and a 1-stage object detector for banknote serial number recognition is used. Full article

We present a GAN-based model that rotates the faces in artistic portraits to various angles. We build a dataset of artistic portraits for training our GAN-based model by applying a 3D face model to the artistic portraits. We also devise proper loss functions to preserve the styles in the artistic portraits as well as to rotate the faces in the portraits to proper angles. These approaches enable us to construct a GAN-based face rotation model. We apply this model to various artistic portraits, including photorealistic oil paint portraits, watercolor portraits, well-known portrait artworks and banknote portraits, and produce convincing rotated faces in the artistic portraits. Finally, we prove that our model can produce improved results compared with the existing models by evaluating the similarity and the angles of the rotated faces through evaluation schemes including FID estimation, recognition ratio estimation, pose estimation and user study. Full article

The identification of printed materials is a critical and challenging issue for security purposes, especially when it comes to documents such as banknotes, tickets, or rare collectable cards: eligible targets for ad hoc forgery. State-of-the-art methods require expensive and specific industrial equipment, while a low-cost, fast, and reliable solution for document identification is increasingly needed in many contexts. This paper presents a method to generate a robust fingerprint, by the extraction of translucent patterns from paper sheets, and exploiting the peculiarities of binary pattern descriptors. A final descriptor is generated by employing a block-based solution followed by principal component analysis (PCA), to reduce the overall data to be processed. To validate the robustness of the proposed method, a novel dataset was created and recognition tests were performed under both ideal and noisy conditions. Full article

Recently, some state-of-the-art works have used deep learning-based architectures, specifically convolutional neural networks (CNNs), for banknote recognition and counterfeit detection with promising results. However, it is not clear which design strategy is more appropriate (custom or by transfer learning) in terms of classifier performance and inference times for massive data applications. This paper presents a comparison of the two design strategies in various types of architecture. For the transfer learning (TL) strategy, the most appropriate freezing points in CNN architectures (sequential, residual and Inception) are identified. In addition, a custom model based on an AlexNet-type sequential CNN is proposed. Both the TL and the custom models were trained and compared using a Colombian banknote dataset. According to the results, ResNet18 achieved the best accuracy, with 100%. On the other hand, the network with the shortest inference times was the proposed custom network, since its performance is up to 6.48-times faster in CPU and 16.29-times faster in GPU than the inference time with the models by transfer learning. Full article

Near-infrared (NIR) images are very useful in many image processing applications, including banknote recognition, vein detection, and surveillance, to name a few. To acquire the NIR image together with visible range signals, an imaging device should be able to simultaneously capture NIR and visible range images. An implementation of such a system having separate sensors for NIR and visible light has practical shortcomings due to its size and hardware cost. To overcome this, a single sensor-based acquisition method is investigated in this paper. The proposed imaging system is equipped with a conventional color filter array of cyan, magenta, yellow, and green, and achieves signal separation by applying a proposed separation matrix which is derived by mathematical modeling of the signal acquisition structure. The elements of the separation matrix are calculated through color space conversion and experimental data. Subsequently, an additional denoising process is implemented to enhance the quality of the separated images. Experimental results show that the proposed method successfully separates the acquired mixed image of visible and near-infrared signals into individual red, green, and blue (RGB) and NIR images. The separation performance of the proposed method is compared to that of related work in terms of the average peak-signal-to-noise-ratio (PSNR) and color distance. The proposed method attains average PSNR value of 37.04 and 33.29 dB, respectively for the separated RGB and NIR images, which is respectively 6.72 and 2.55 dB higher than the work used for comparison. Full article

We investigated a machine-learning-based fast banknote serial number recognition method. Unlike existing methods, the proposed method not only recognizes multi-digit serial numbers simultaneously but also detects the region of interest for the serial number automatically from the input image. Furthermore, the proposed method uses knowledge distillation to compress a cumbersome deep-learning model into a simple model to achieve faster computation. To automatically decide hyperparameters for knowledge distillation, we applied the Bayesian optimization method. In experiments using Japanese Yen, Korean Won, and Euro banknotes, the proposed method showed significant improvement in computation time while maintaining a performance comparable to a sequential region of interest (ROI) detection and classification method. Full article

We investigated machine learning-based joint banknote recognition and counterfeit detection method. Unlike existing methods, since the proposed method simultaneously recognize banknote type and detect counterfeit detection, it is significantly faster than existing serial banknote recognition and counterfeit detection methods. Furthermore, we propose an explainable artificial intelligence method for visualizing regions that contributed to the recognition and detection. Using the visualization, it is possible to understand the behavior of the trained machine learning system. In experiments using the United State Dollar and the European Union Euro banknotes, the proposed method shows significant improvement in computation time from conventional serial method. Full article

Automatic sorting of banknotes in payment facilities, such as automated payment machines or vending machines, consists of many tasks such as recognition of banknote type, classification of fitness for recirculation, and counterfeit detection. Previous studies addressing these problems have mostly reported separately on each of these classification tasks and for a specific type of currency only. In other words, there has been little research conducted considering a combination of these multiple tasks, such as classification of banknote denomination and fitness of banknotes, as well as considering a multinational currency condition of the method. To overcome this issue, we propose a multinational banknote type and fitness classification method that both recognizes the denomination and input direction of banknotes and determines whether the banknote is suitable for reuse or should be replaced by a new one. We also propose a method for estimating the fitness value of banknotes and the consistency of the estimation results among input trials of a banknote. Our method is based on a combination of infrared-light transmission and visible-light reflection images of the input banknote and uses deep-learning techniques with a convolutional neural network. The experimental results on a dataset composed of Indian rupee (INR), Korean won (KRW), and United States dollar (USD) banknote images with mixture of two and three fitness levels showed that the proposed method gives good performance in the combination condition of currency types and classification tasks. Full article

Automatic recognition of banknotes is applied in payment facilities, such as automated teller machines (ATMs) and banknote counters. Besides the popular approaches that focus on studying the methods applied to various individual types of currencies, there have been studies conducted on simultaneous classification of banknotes from multiple countries. However, their methods were conducted with limited numbers of banknote images, national currencies, and denominations. To address this issue, we propose a multi-national banknote classification method based on visible-light banknote images captured by a one-dimensional line sensor and classified by a convolutional neural network (CNN) considering the size information of each denomination. Experiments conducted on the combined banknote image database of six countries with 62 denominations gave a classification accuracy of 100%, and results show that our proposed algorithm outperforms previous methods. Full article

Despite a decrease in the use of currency due to the recent growth in the use of electronic financial transactions, real money transactions remain very important in the global market. While performing transactions with real money, touching and counting notes by hand, is still a common practice in daily life, various types of automated machines, such as ATMs and banknote counters, are essential for large-scale and safe transactions. This paper presents studies that have been conducted in four major areas of research (banknote recognition, counterfeit banknote detection, serial number recognition, and fitness classification) in the accurate banknote recognition field by various sensors in such automated machines, and describes the advantages and drawbacks of the methods presented in those studies. While to a limited extent some surveys have been presented in previous studies in the areas of banknote recognition or counterfeit banknote recognition, this paper is the first of its kind to review all four areas. Techniques used in each of the four areas recognize banknote information (denomination, serial number, authenticity, and physical condition) based on image or sensor data, and are actually applied to banknote processing machines across the world. This study also describes the technological challenges faced by such banknote recognition techniques and presents future directions of research to overcome them. Full article