A comprehensive survey on robust image watermarking

In order to enhance the security of exchanged medical images in telemedicine, we propose in this paper a blind and robust approach for medical image protection. This approach consists in embedding patient information and image acquisition data in the image. This imperceptible integration must generate the least possible distortion. The watermarked image must present the same clinical reading as the original image. The proposed approach is applied in the frequency domain. For this purpose, four transforms were used: discrete wavelets transform, non-subsampled contourlet transform, non-subsampled shearlet transform and discreet cosine transform. All these transforms was combined with Schur decomposition and the watermark bits were integrated in the upper triangular matrix. To obtain a satisfactory compromise between robustness and imperceptibility, the integration was performed in the medium frequencies of the image. Imperceptibility and robustness experimental results shows that the proposed methods maintain a high quality of watermarked images and are remarkably robust against several conventional attacks.

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A robust blind medical image watermarking approach for telemedicine applications

The increasing number of phishing attacks is one of the major concerns of security researchers today. The traditional tools for identifying phishing websites use signature-based approaches which are not able to detect newly created phishing webpages. Thus, researchers are coming up with machine learning-based methods which are capable to detect and classify the phishing webpages with high accuracy if a large and variety of features are considered. However, building a classification model using a large number of features takes time which hampers the timely detection of phishing websites. Therefore, it is pertinent to shortlist a set of features using a feature selection method so that high-performance classification models can be developed in less time. In this chapter, we study the role of feature selection methods in detecting phishing webpages efficiently and effectively. A comparative analysis of machine learning algorithms is carried out on the basis of their performance without and with feature selection. Experiments are conducted on a phishing dataset with 30 features containing 4898 phishing and 6157 benign webpages. Several machine learning algorithms are used for obtaining the best results. Afterward, a feature selection method is employed to improve the efficiency of the models. The best accuracy is obtained by random forest both before and after feature selection with a significant improvement in model building time. The experiments demonstrate that employing a feature selection method along with machine learning algorithms can improve the build time of classification models for phishing detection without compromising their accuracy.

An Efficient Approach for Phishing Detection using Machine Learning

In recent years, advancements in Internet and cloud technologies have led to a significant increase in electronic trading in which consumers make online purchases and transactions. This growth leads to unauthorized access to users’ sensitive information and damages the resources of an enterprise. Phishing is one of the familiar attacks that trick users to access malicious content and gain their information. In terms of website interface and uniform resource locator (URL), most phishing webpages look identical to the actual webpages. Various strategies for detecting phishing websites, such as blacklist, heuristic, Etc., have been suggested. However, due to inefficient security technologies, there is an exponential increase in the number of victims. The anonymous and uncontrollable framework of the Internet is more vulnerable to phishing attacks. Existing research works show that the performance of the phishing detection system is limited. There is a demand for an intelligent technique to protect users from the cyber-attacks. In this study, the author proposed a URL detection technique based on machine learning approaches. A recurrent neural network method is employed to detect phishing URL. Researcher evaluated the proposed method with 7900 malicious and 5800 legitimate sites, respectively. The experiments’ outcome shows that the proposed method’s performance is better than the recent approaches in malicious URL detection.

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Detecting phishing websites using machine learning technique.

This paper proposes an encryption-based image watermarking scheme using a combination of second-level discrete wavelet transform (2DWT) and discrete cosine transform (DCT) with an auto extraction feature. The 2DWT has been selected based on the analysis of the trade-off between imperceptibility of the watermark and embedding capacity at various levels of decomposition. DCT operation is applied to the selected area to gather the image coefficients into a single vector using a zig-zig operation. We have utilized the same random bit sequence as the watermark and seed for the embedding zone coefficient. The quality of the reconstructed image was measured according to bit correction rate, peak signal-to-noise ratio (PSNR), and similarity index. Experimental results demonstrated that the proposed scheme is highly robust under different types of image-processing attacks. Several image attacks, e.g., JPEG compression, filtering, noise addition, cropping, sharpening, and bit-plane removal, were examined on watermarked images, and the results of our proposed method outstripped existing methods, especially in terms of the bit correction ratio (100%), which is a measure of bit restoration. The results were also highly satisfactory in terms of the quality of the reconstructed image, which demonstrated high imperceptibility in terms of peak signal-to-noise ratio (PSNR ≥ 40 dB) and structural similarity (SSIM ≥ 0.9) under different image attacks.

Encryption Based Image Watermarking Algorithm in 2DWT-DCT Domains

In today’s knowledge driven society and technology driven economy, when data is considered to be one of the most important corporate resource of an organization, equally important is the security of the same to prevent unauthorized users to access and manipulate it. Watermarking being an appropriate solution for claiming authorization has prompted many researchers to work in this direction and accordingly a substantial amount of research work has been done using different schemes on various media types. The main impetus in writing this paper is to provide a broader view as how much work has been carried so far and what are the different dimensions that have been taken into consideration to watermark color images using discrete wavelet transformation. The paper will help prospective researchers to gain more insight about the work that has been done and pave a way for them to design and implement better techniques.

DWT based color image watermarking: a review

The growth and emergence of telemedicine has resulted into considerable increase in the transfer of medical images between various health centers over computer networks for various purposes, such as diagnosis, clinical interpretation, and archives. The security of these images is a serious concern since these images can be manipulated during their transmission over the network. Moreover, as the authentication of the medical images is required for various legal purposes; the information security requirement specifications specified by various legislative rules must be adhered by the concerned stakeholders. In this research, a discrete wavelet-based watermarking scheme has been proposed for the authentication of medical images. The experimental results obtained have shown that the proposed scheme is a secure one and robust against various common image processing attacks.

A Discrete Wavelet Based Watermarking Scheme for Authentication of Medical Images

In recent years, the growth of the Internet along with multimedia data delivery has caused the misuse and even alteration of the original digital content. Multimedia data has a big share of the total data transmitted over the Internet every day. Such multimedia data is vulnerable to misuse and theft. Digital watermarking has been used traditionally to protect multimedia data by authenticating the content and by providing copyright protection. The downside of digital watermarking, however, is that it always introduces some amount of signal distortion. The distortion caused is imperceptible and doesn’t create an issue in general. But, in certain applications like the medical field, such loss of information is intolerable because the distorted information might affect the analysis of medical images and the subsequent inferences drawn from this analysis. Reversible watermarking techniques ensure that there is no information loss in the watermarking process and as such are suitable for such applications. This chapter presents an overview of reversible digital watermarking, the classification of different reversible watermarking techniques with special emphasis on the state-of-the-art reversible watermarking techniques developed for maintaining the integrity and authenticity of medical image data.

Survey on Reversible Watermarking Techniques for Medical Images

A Generic Analogy-Centered Software Cost Estimation Based on Differential Evolution Exploration Process

Background: Social Network Analysis is a prominent field of research that captures the attention of numerous data mining expert. Social networks are visualized as network graphs, and identifying communities involves the identification of densely connected nodes. The exploration of community detection in online social networks is an essential field of research. This review paper presents an extensive examination of the latest methodologies and approaches utilized for isolated and overlapped community detection specifically in online social networks. Objective: To provide a comprehensive overview of the existing literature on community detection techniques for social networks with isolated and overlapped communities. The review aims to identify the key challenges associated with community detection in such networks and to review the various algorithms and methods that have been proposed to address these challenges. Additionally, this review intends to compare the performance of different community detection techniques on networks with isolated and overlapped communities, and to highlight their strengths and weaknesses. Ultimately, the goal of the review is to provide researchers and practitioners with a better understanding of the current state of the art in community detection for social networks, and to help guide future research in this important area. Methods: A comprehensive literature search was conducted on quality databases viz. Scopus, Web of Science, IEEE Xplore, and Science Direct using relevant keywords, and the selected articles were screened for inclusion/ exclusion based on their titles, abstracts, and keywords. The following are some search keywords that can be used for searching community detection papers: Community detection, Graph clustering, Network partitioning, Modularity optimization, Community structure, Graph-based clustering, Network community detection, Overlapping communities, Community detection algorithms, and Evaluation metrics for community detection. Comparative analysis was performed on the chosen articles, considering factors such as algorithms, methodologies, datasets, and evaluation metrics. The results were presented using comparative tables to present the findings. The methodology ensured a comprehensive review of recent literature on community detection, providing valuable insights and trends in the field. Findings: The review identified various algorithms and methods that have been proposed to address these challenges, including modularity optimization, spectral clustering, label propagation, and network embeddings. The paper also found that many of these algorithms can be adapted to handle networks with isolated and overlapped communities, but there is no single algorithm that is universally effective. The review highlighted that the performance of community detection techniques can be influenced by several factors, such as network size, density, and community structure. The paper found that some algorithms perform better than others in certain scenarios and that a combination of methods may be necessary to achieve optimal results. The review found that there are several challenges associated with community detection in such networks, including the presence of noise, sparsity, and overlapping communities. Novelty: This paper presents several novel contributions to the field of community detection in social networks. One key novelty of the paper is its comprehensive review of the existing literature on community detection techniques for networks with isolated and overlapped communities, which provides a valuable resource for researchers and practitioners working in this area. Another novelty of the paper is its identification of the key challenges associated with community detection in such networks, and its discussion of the various algorithms and methods that have been proposed to address these challenges. The paper also presents a comparison of the performance of different community detection techniques on networks with isolated and overlapped communities, which can help guide future research in this area. Finally, the paper proposes several new approaches for community detection in social networks with complex community structures, which can help advance our understanding of social networks and their potential as powerful tools for communication, collaboration, and information sharing. Overall, the paper’s novel contributions make a significant contribution to the field of community detection in social networks. Keywords: Social network analysis; community detection; influential user; overlapping communities; dynamic networks 

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Javaid Iqbal Bhat

Papers

DWT based color image watermarking: a review

A Discrete Wavelet Based Watermarking Scheme for Authentication of Medical Images

Survey on Reversible Watermarking Techniques for Medical Images

A Generic Analogy-Centered Software Cost Estimation Based on Differential Evolution Exploration Process

Unlocking the Power of Social Networks with Community Detection Techniques for Isolated and Overlapped Communities: A Review