The concealment of data has emerged as an area of deep and wide interest in research that endeavours to conceal data in a covert and stealth manner, to avoid detection through the embedment of the secret data into cover images that appear inconspicuous. These cover images may be in the format of images or videos used for concealment of the messages, yet still retaining the quality visually. Over the past ten years, there have been numerous researches on varying steganographic methods related to images, that emphasised on payload and the quality of the image. Nevertheless, a compromise exists between the two indicators and to mediate a more favourable reconciliation for this duo is a daunting and problematic task. Additionally, the current techniques have not been successful in attaining more improved security caused by the non-encrypted data that only underwent the first layer of concealment through merely a straightforward embedment process of the secret data within the images, thus allowing the extraction of the concealed data to be quite simple for hostile entities. Hence, in the current study, the proposed scheme, we have improved the Bit Inverting Map method to narrow the gap of existing work. Our experimental results indicate that the proposed framework maintains a better balance between image visual quality and security, with relatively less computational and complexity, which assures its effectiveness compared to other state-of-the-art methods.
Medical image segmentation is one of the most actively studied fields in the past few decades, as the development of modern imaging modalities such as magnetic resonance imaging (MRI) and computed tomography (CT), physicians and technicians nowadays have to process the increasing number and size of medical images. Therefore, efficient and accurate computational segmentation algorithms become necessary to extract the desired information from these large data sets. Moreover, sophisticated segmentation algorithms can help the physicians delineate better the anatomical structures presented in the input images, enhance the accuracy of medical diagnosis and facilitate the best treatment planning. Many of the proposed algorithms could perform w
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Image retrieval is used in searching for images from images database. In this paper, content – based image retrieval (CBIR) using four feature extraction techniques has been achieved. The four techniques are colored histogram features technique, properties features technique, gray level co- occurrence matrix (GLCM) statistical features technique and hybrid technique. The features are extracted from the data base images and query (test) images in order to find the similarity measure. The similarity-based matching is very important in CBIR, so, three types of similarity measure are used, normalized Mahalanobis distance, Euclidean distance and Manhattan distance. A comparison between them has been implemented. From the results, it is conclud
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This paper presents the matrix completion problem for image denoising. Three problems based on matrix norm are performing: Spectral norm minimization problem (SNP), Nuclear norm minimization problem (NNP), and Weighted nuclear norm minimization problem (WNNP). In general, images representing by a matrix this matrix contains the information of the image, some information is irrelevant or unfavorable, so to overcome this unwanted information in the image matrix, information completion is used to comperes the matrix and remove this unwanted information. The unwanted information is handled by defining {0,1}-operator under some threshold. Applying this operator on a given ma
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In this paper two main stages for image classification has been presented. Training stage consists of collecting images of interest, and apply BOVW on these images (features extraction and description using SIFT, and vocabulary generation), while testing stage classifies a new unlabeled image using nearest neighbor classification method for features descriptor. Supervised bag of visual words gives good result that are present clearly in the experimental part where unlabeled images are classified although small number of images are used in the training process.
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Pavement crack and pothole identification are important tasks in transportation maintenance and road safety. This study offers a novel technique for automatic asphalt pavement crack and pothole detection which is based on image processing. Different types of cracks (transverse, longitudinal, alligator-type, and potholes) can be identified with such techniques. The goal of this research is to evaluate road surface damage by extracting cracks and potholes, categorizing them from images and videos, and comparing the manual and the automated methods. The proposed method was tested on 50 images. The results obtained from image processing showed that the proposed method can detect cracks and potholes and identify their severity levels wit
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With the proliferation of both Internet access and data traffic, recent breaches have brought into sharp focus the need for Network Intrusion Detection Systems (NIDS) to protect networks from more complex cyberattacks. To differentiate between normal network processes and possible attacks, Intrusion Detection Systems (IDS) often employ pattern recognition and data mining techniques. Network and host system intrusions, assaults, and policy violations can be automatically detected and classified by an Intrusion Detection System (IDS). Using Python Scikit-Learn the results of this study show that Machine Learning (ML) techniques like Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) can enhance the effectiveness of an Intrusi
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The current research aims to study the extent to which the Independent High Electoral Commission applies to information security risk management by the international standard (ISO / IEC27005) in terms of policies, administrative and technical procedures, and techniques used in managing information security risks, based on the opinions of experts in the sector who occupy positions (General Manager The directorate, department heads and their agents, project managers, heads of divisions, and those authorized to access systems and software). The importance of the research comes by giving a clear picture of the field of information security risk management in the organization in question because of its significant role in identifying risks and s
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