Counterfeit coins pose a significant challenge in various real-world applications, from vending machines to currency exchange systems, making their reliable detection a matter of utmost importance. This research presents a novel framework designed to tackle this issue by harnessing the power of image-mining techniques. Our proposed framework is developed in two modules. In the first module, a method to detect the region of interest (ROIs) is applied that focuses on blob detection. In the second module, image mining is applied to find image patterns present in coin images using fuzzy association rules mining. The enhancement lies in utilizing Particle Swarm Optimization (PSO) within the image mining module. PSO refines the threshold parameters, thereby improving the efficiency of the fuzzy association rules mining process. This integration allows for the automatic determination of optimal values, contributing to the overall robustness of the counterfeit coin detection system. Comprising two modules, this framework offers a unique advantage as a compress, serving as a knowledge attainment tool. By harnessing the full power of fuzzy association rule mining, this paper introduces pruning methods to reduce redundant and insignificant rules. Moreover, we propose a novel algorithm for feature selection and a pruned-based fuzzy associative classifier to establish a robust counterfeit coin detection system. Comparative analysis with other methods using the same dataset showcases the superiority of our framework, exhibiting lower feature dimensions, smoother boundaries, and maintaining satisfactory accuracy. The generality of this study's problem formulation offers a common framework for addressing similar challenges across various domains.

Detecting a counterfeit coin using 2D image processing is nearly impossible in some cases, especially when the coin is damaged, corroded or worn out. Edge detection is one of the most widely used techniques to extract features from 2D images. However, in 2D images, the height information is missing, losing the hidden characteristics. In this paper, we propose a 3D approach to detect and analyze the precipice borders from the coin surface and extract significant features to train an ensemble classification system. To extract the features, we also propose Binned Borders in Spherical Coordinates (BBSC) to analyze different parts of precipice borders at different polar and azimuthal angles. The proposed method is robust even against degradation which appears on shiny coins after 3D scanning. Therefore, there is no need to restore the degraded images before the feature extraction process. Here, the system has been trained and tested with four types of Danish and two types of Chinese coins. We take advantage of stack generalization to classify the coins and add the reject option to increase the reliability of the system. The results illustrate that the proposed method outperforms other counterfeit coin detectors. The accuracy obtained by testing Danish 1990, 1991, 1996, and 2008 datasets are 98.6%, 98.0%, 99.8%, and 99.9% respectively. In addition, results for half Yuan Chinese 1942 and one Yuan Chinese 1997 were 95.5% and 92.2% respectively.

In this research, we take advantage of deep learning approaches to improve the performance of counterfeit coin detection. As most of the pre-trained networks accept three channels for their input, we propose a new method to represent the relief map (height-map image) in three geometrical forms with Steep, Moderate, and Gentle slopes. This can also make our proposed method more understandable for both coin and AI experts by an eXplainable AI strategy. We compensate for the lack of fake coins by using a proposed Generative Adversarial Network. Then, we proposed a hybrid method using fine-tuning pre-trained deep neural networks to detect fake coins and provide a rejection option to increase the reliability of the system. While a few fake coins available in this research are used in the training process, the model is mostly trained by the images that are generated as fake coins from genuine ones. However, the system produces remarkable results to classify the coins.

Image processing techniques using the knowledge obtained from known historical data has become recently one of the most intensively studied topics in decision science and computer science. This paper presents an automatic system for fake coins detection based on image content. In this study, a blob detector image-based method by fuzzy association rules mining is proposed to detect counterfeit coins. This method consists of two-stages. In the first stage, the original image dataset is preprocessed by a blob detector. This provides all frequent features that must be mined in the next stage. In the second stage, fuzzy association rules mining extracts the effective fuzzy rules and classifies automatically the coin image data. The performance of the proposed method has been compared with some other methods and we demonstrate that our framework surpasses in terms of classification accuracy, which is a desirable level when compared with recent studies in this field. This research demonstrates the proposed framework is a reliable intelligent detection system and can be utilized for other applications based on image content.

There are presently a lot of companies, museums, and government agencies in the world which have increased the demand of automatic systems to classify precious, historical, and common coins. Thanks to the increased exigency on the intelligent approaches to recognize counterfeit coins, coin detection has been constantly evolving in recent years and image-based coin recognition has become an integral part of this research area. In this research, we have been studying various methods related to image-based coin detection in 2-D image processing and we also used a 3-D scanner to scan hundreds of coins to apply 3-D approaches to detect counterfeit coins. Here, instead of conventional 2-D methods for counterfeit coin detection, we applied a 3-D approach to model and analyze a large set of different Danish coins. One of the most important advantages of 3-D approaches is the ability to extract features which cannot be found in 2-D images. Despite this advantage, we had a lot of unexpected degradations on shiny coin images. In order to restore the degraded images, we proposed a method based on signal separation in this paper. The experimental results showed that the proposed method outperformed previous methods in detection of counterfeit coins.

In this paper, a new framework is proposed for counterfeit coin detection that shows the effectiveness of image mining techniques in this field. The proposed system is developed in two modules. At the first module, the segmentation of digital coin images is applied to find the region of interest (ROI). These ROIs serve as inputs to the next stage. At the next module, image mining is applied to find frequent image patterns present in coin images using fuzzy association rules mining. The experimental results show that fuzzy association rules mining based on image content is feasible and gives strong rules that can be further used for effective classification of coin images.

In image-based coin detection, making the image readable is an indispensable part of the feature extraction. However using a 2-D image processing approach for detecting a counterfeit coin is nearly impossible in case of destroyed coins whose textures are severely burnt, sulfated, rusted, or colored. In this research, we used a 3-D scanner to scan and model an acceptable number of coins capturing height and depth instead of levels of color. The most important advantage of 3-D scanning is to compensate for the above-mentioned destructions of the coin surface. Despite this advantage, we had several unexpected degradations due to shiny coin images. To solve this problem, the 3-D image was decomposed column-wise to a number of separate 1-D signals, which were analyzed separately and restored by the proposed method. This approach gave remarkable results when used to extract valuable features.

Feature selection is one of the most important techniques for data preprocessing in classification problems. In this paper, fuzzy grids–based association rules mining, as an effective data mining technique, is used for feature selection in misuse detection application in computer networks. The main idea of this algorithm is to find the relationships between items in large datasets so that it detects correlations between inputs of the system and then eliminates the redundant inputs. To classify the attacks, a fuzzy ARTMAP neural network is employed whose training parameters are optimized by gravitational search algorithm. The performance of the proposed system is compared with some other machine learning methods in the same application. Experimental results show that the proposed system, when choosing optimum “feature subset size-adjustment” parameter, performs better in terms of detection rate, false alarm rate, and cost per example in classification problems. In addition, employing the reduced-size feature set results in more than 8.4 percent reduction in computational complexity.

Using the fuzzy logic for sampling may be a suitable method for data preprocessing and it improves the efficiency of intrusion detection system. This paper has shown how to use a method based on fuzzy clustering, the training samples will be clustered and separated the inappropriate data from the clusters. Accordingly, the remaining samples are supposed to be very suitable representative of different classes and can have a positive influence on the classification but inappropriate data will not be removed or deleted. In proposed method, the inappropriate data will be labeled with Abnormal Class then in training and test phase we will have one extra class that we called Abnormal. Evaluation of the proposed method is performed by KDDCup99 dataset. Our experimental results indicate that intrusion detection system with the proposed preprocessing has performed better than other systems without preprocessing in the case of classification, precision, recall, f-measure, detection and false alarm rate.

ABSTRACTFeature selection is a classic research topic in data mining, and it has attracted much interest in many fields such as network security. In addition, data mining approaches such as fuzzy association rule mining (FARM) can improve the performance of intrusion detection systems. In this study, a FARM‐based feature selector is proposed in order to reduce the dimension of input features to the misuse detector. Furthermore, a fuzzy ARTMAP neural network is used as the classifier. The accuracy of the proposed approach depends strongly on the precision of the parameters of FARM‐based feature selector module and fuzzy ARTMAP neural classifier. Particle swarm optimization (PSO) algorithm is incorporated into the proposed method to determine optimum values of parameters. In this way, the performance of PSO algorithm is compared with genetic algorithm (GA), as well. Experimental results indicate that PSO outperforms GA both in population size and number of evolutions and can converge faster. This is very important for enhancing the mining performance in large datasets such as intrusion detection datasets. When compared with some other machine learning methods, the proposed system indicates better performance in terms of detection rate, false alarm rate, and cost per example. Copyright © 2012 John Wiley & Sons, Ltd.

Intrusion Detection System (IDS) deals with very large amount of data that includes redundant and irrelevant features. Therefore feature selection is a necessary data pre-processing step to design IDSs that are lightweight. In this paper, a novel feature selection method based on data mining techniques is proposed which uses fuzzy association rules to obtain the optimum feature subset. In this research, the fuzzy ARTMAP neural network is used as the classifier to evaluate the goodness of the obtained feature subset. The effectiveness of proposed method is evaluated by experiments on KDD Cup99 dataset. According to the performance comparisons with some other machine learning methods that have used the same dataset, the proposed method is the most efficient on detection rate, false alarm rate and cost per example.