Despite the huge advances in digital communications in the last decade, physical documents are still the most common media for information transfer, especially in the official context. However, the readily available document processing devices and techniques (printers, scanners, etc.) facilitate the illegal manipulation or imitation of original documents by forgers. Therefore, verification of the authenticity and detection of forgery is of paramount importance to all agencies receiving printed documents. We suggest an unsupervised forgery detection framework that can distinguish whether a document is forged based on the spectroscopy of the document’s ink. The spectra of the tested documents inks (original and questioned) were obtained using laser-induced breakdown spectroscopy (LIBS) technology. Then, a correlation matrix of the spectra was calculated for both the original and questioned documents together, which were then transformed into an adjacency matrix aiming at converting it into a weighted network under the concept of graph theory. Clustering algorithms were then applied to the network to determine the number of clusters. The proposed approach was tested under a variety of scenarios and different types of printers (e.g., inkjet, laser, and photocopiers) as well as different kinds of papers. The findings show that the proposed approach provided a high rate of accuracy in identifying forged documents and a high detection speed. It also provides a visual output that is easily interpretable to the non-expert, which provides great flexibility for real-world application.

The area of forgery detection of documents is considered an active field of research in digital forensics. One of the most common issues that investigators struggle with is circled around the selection of the approach in terms of accuracy, complexity, cost, and ease of use. The literature includes many approaches that are based on either image processing techniques or spectrums analysis. However, most of the available approaches have issues related to complexity and accuracy. This article suggests an unsupervised forgery detection framework that utilizes the correlations among the spectrums of documents’ matters in generating a weighted network for the tested documents. The network, then, is clustered using several unsupervised clustering algorithms. The detection rate is measured according to the number of network clusters. Based on the obtained results, our approach provides high accuracy using the Louvain clustering algorithms, while the use of the updated version of the DBSAN was more successful when testing many documents at the same time. Additionally, the suggested framework is considered simple to implement and does not require professional knowledge to use.

Printed documents continue to be the most commonly used media for information transfer in official context. However, such documents may be subject to illegitimate modification or malicious purposes. Therefore, agencies must be able to verify their authenticity before accepting them. This work aims to identify and classify the current computational methods that are used to identify and detect a forgery in printed documents. We focus on methods that detect forger passively, i.e., no requirement of active information to be included in the document during or shortly after printing. Hence, we classify the methods into four general categories. First, detection based on printer and ink properties. The second is based on character-level features analysis that is based on detecting imperfections produced by scan, edit, and print forgery techniques. It is also possible to detect forgery through analysis of paper properties. Finally, forgery detection can be achieved by analyzing the pixel-level features.

By Using data mining techniques, much study has been undertaken to derive information from medical data, which may enhance the curing ability. This study has been compared four automated methods for blood diseases detection using a different method for attributes reduction methods: PCA (Principal Component Analysis), ICA (Independent Component Analysis), original data set, and four algorithms, which are Naive Bayes, K-Nearest Neighbor, Decision Tree, and SVM (Support Vector Machine) for classification. The results show that after applying the PCA technique, the accuracy is: 0.98, 1.0, 0.79, and 0.78 for Naive Bayes, Decision Tree, SVM, and KNN and after applying the ICA technique, the accuracy is 0.76,.76, 0.76, and 0.74 for Naive Bayes, Decision Tree, SVM, and KNN, while the accuracy after applying on the original data set is: 0.98, 1.0, 0.78, and 0.75 for Naive Bayes, Decision Tree, SVM, and KNN. The results appeared that when applying decision tree on the original data set and after applying the PCA method, have been gotten the highest accuracy which is 100 %.

Iraqi Correction Services (ICS) is a department within the Ministry of Justice. Most services are provided through traditional paper and pen system. This system causes delays in service provision time and work over-load on ICS staff which translates into a high cost of service provision. E-government applications represent an opportunity to automate governmental services while improving both effectiveness and efficiency. This work presents the approach taken to determine the priority to automate ICS service from the perspective of service beneficiaries. The research is yet to be completed, however, up to this point we developed four different questionnaire-sets in order to collect the requirements from the shareholders, verified them and implemented the actual survey in the field. The experience gained throughout these steps and the future analysis plan is reported in this paper.