Digital watermarking : applicability for developing trust in medical imaging workflows state of the art review

Medical images can be intentionally or unintentionally manipulated both within the secure medical system environment and outside, as images are viewed, extracted and transmitted. Many organisations have invested heavily in Picture Archiving and Communication Systems (PACS), which are intended to facilitate data security. However, it is common for images, and records, to be extracted from these for a wide range of accepted practices, such as external second opinion, transmission to another care provider, patient data request, etc. Therefore, confirming trust within medical imaging workflows has become essential. Digital watermarking has been recognised as a promising approach for ensuring the authenticity and integrity of medical images. Authenticity refers to the ability to identify the information origin and prove that the data relates to the right patient. Integrity means the capacity to ensure that the information has not been altered without authorisation. This paper presents a survey of medical images watermarking and offers an evident scene for concerned researchers by analysing the robustness and limitations of various existing approaches. This includes studying the security levels of medical images within PACS system, clarifying the requirements of medical images watermarking and defining the purposes of watermarking approaches when applied to medical images

Tamper detection of qur'anic text watermarking scheme based on vowel letters with Kashida using exclusive-or and queueing technique

The most sensitive Arabic text available online is the digital Holy Qur’an. This sacred Islamic religious book is recited by all Muslims worldwide including the non-Arabs as part of their worship needs. It should be protected from any kind of tampering to keep its invaluable meaning intact. Different characteristics of the Arabic letters like the vowels ( أ . و . ي ), Kashida (extended letters), and other symbols in the Holy Qur’an must be secured from alterations. The cover text of the al-Qur’an and its watermarked text are different due to the low values of the Peak Signal to Noise Ratio (PSNR), Embedding Ratio (ER), and Normalized Cross-Correlation (NCC), thus the location for tamper detection gets low accuracy. Watermarking technique with enhanced attributes must therefore be designed for the Qur’an text using Arabic vowel letters with Kashida. Most of the existing detection methods that tried to achieve accurate results related to the tampered Qur’an text often show various limitations like diacritics, alif mad surah, double space, separate shapes of Arabic letters, and Kashida. The gap addressed by this research is to improve the security of Arabic text in the Holy Qur’an by using vowel letters with Kashida. The purpose of this research is to enhance Quran text watermarking scheme based on exclusive-or and reversing with queueing techniques. The methodology consists of four phases. The first phase is pre-processing followed by the embedding process phase to hide the data after the vowel letters wherein if the secret bit is ‘1’, insert the Kashida but do not insert it if the bit is ‘0’. The third phase is extraction process and the last phase is to evaluate the performance of the proposed scheme by using PSNR (for the imperceptibility), ER (for the capacity), and NCC (for the security of the watermarking). The experimental results revealed the improvement of the NCC by 1.77 %, PSNR by 9.6 %, and ER by 8.6 % compared to available current schemes. Hence, it can be concluded that the proposed scheme has the ability to detect the location of tampering accurately for attacks of insertion, deletion, and reordering

Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis

Information Analysis for Steganography and Steganalysis in 3D Polygonal Meshes

Information hiding, which embeds a watermark/message over a cover signal, has recently found extensive applications in, for example, copyright protection, content authentication and covert communication. It has been widely considered as an appealing technology to complement conventional cryptographic processes in the field of multimedia security by embedding information into the signal being protected. Generally, information hiding can be classified into two categories: steganography and watermarking. While steganography attempts to embed as much information as possible into a cover signal, watermarking tries to emphasize the robustness of the embedded information at the expense of embedding capacity. In contrast to information hiding, steganalysis aims at detecting whether a given medium has hidden message in it, and, if possible, recover that hidden message. It can be used to measure the security performance of information hiding techniques, meaning a steganalysis resistant steganographic/watermarking method should be imperceptible not only to Human Vision Systems (HVS), but also to intelligent analysis. As yet, 3D information hiding and steganalysis has received relatively less attention compared to image information hiding, despite the proliferation of 3D computer graphics models which are fairly promising information carriers. This thesis focuses on this relatively neglected research area and has the following primary objectives: 1) to investigate the trade-off between embedding capacity and distortion by considering the correlation between spatial and normal/curvature noise in triangle meshes; 2) to design satisfactory 3D steganographic algorithms, taking into account this trade-off; 3) to design robust 3D watermarking algorithms; 4) to propose a steganalysis framework for detecting the existence of the hidden information in 3D models and introduce a universal 3D steganalytic method under this framework. %and demonstrate the performance of the proposed steganalysis by testing it against six well-known 3D steganographic/watermarking methods. The thesis is organized as follows. Chapter 1 describes in detail the background relating to information hiding and steganalysis, as well as the research problems this thesis will be studying. Chapter 2 conducts a survey on the previous information hiding techniques for digital images, 3D models and other medium and also on image steganalysis algorithms. Motivated by the observation that the knowledge of the spatial accuracy of the mesh vertices does not easily translate into information related to the accuracy of other visually important mesh attributes such as normals, Chapters 3 and 4 investigate the impact of modifying vertex coordinates of 3D triangle models on the mesh normals. Chapter 3 presents the results of an empirical investigation, whereas Chapter 4 presents the results of a theoretical study. Based on these results, a high-capacity 3D steganographic algorithm capable of controlling embedding distortion is also presented in Chapter 4. In addition to normal information, several mesh interrogation, processing and rendering algorithms make direct or indirect use of curvature information. Motivated by this, Chapter 5 studies the relation between Discrete Gaussian Curvature (DGC) degradation and vertex coordinate modifications. Chapter 6 proposes a robust watermarking algorithm for 3D polygonal models, based on modifying the histogram of the distances from the model vertices to a point in 3D space. That point is determined by applying Principal Component Analysis (PCA) to the cover model. The use of PCA makes the watermarking method robust against common 3D operations, such as rotation, translation and vertex reordering. In addition, Chapter 6 develops a 3D specific steganalytic algorithm to detect the existence of the hidden messages embedded by one well-known watermarking method. By contrast, the focus of Chapter 7 will be on developing a 3D watermarking algorithm that is resistant to mesh editing or deformation attacks that change the global shape of the mesh. By adopting a framework which has been successfully developed for image steganalysis, Chapter 8 designs a 3D steganalysis method to detect the existence of messages hidden in 3D models with existing steganographic and watermarking algorithms. The efficiency of this steganalytic algorithm has been evaluated on five state-of-the-art 3D watermarking/steganographic methods. Moreover, being a universal steganalytic algorithm can be used as a benchmark for measuring the anti-steganalysis performance of other existing and most importantly future watermarking/steganographic algorithms. Chapter 9 concludes this thesis and also suggests some potential directions for future work

Contourlet Domain Image Modeling and its Applications in Watermarking and Denoising

Statistical image modeling in sparse domain has recently attracted a great deal of research interest. Contourlet transform as a two-dimensional transform with multiscale and multi-directional properties is known to effectively capture the smooth contours and geometrical structures in images. The objective of this thesis is to study the statistical properties of the contourlet coefficients of images and develop statistically-based image denoising and watermarking schemes. Through an experimental investigation, it is first established that the distributions of the contourlet subband coefficients of natural images are significantly non-Gaussian with heavy-tails and they can be best described by the heavy-tailed statistical distributions, such as the alpha-stable family of distributions. It is shown that the univariate members of this family are capable of accurately fitting the marginal distributions of the empirical data and that the bivariate members can accurately characterize the inter-scale dependencies of the contourlet coefficients of an image. Based on the modeling results, a new method in image denoising in the contourlet domain is proposed. The Bayesian maximum a posteriori and minimum mean absolute error estimators are developed to determine the noise-free contourlet coefficients of grayscale and color images. Extensive experiments are conducted using a wide variety of images from a number of databases to evaluate the performance of the proposed image denoising scheme and to compare it with that of other existing schemes. It is shown that the proposed denoising scheme based on the alpha-stable distributions outperforms these other methods in terms of the peak signal-to-noise ratio and mean structural similarity index, as well as in terms of visual quality of the denoised images. The alpha-stable model is also used in developing new multiplicative watermark schemes for grayscale and color images. Closed-form expressions are derived for the log-likelihood-based multiplicative watermark detection algorithm for grayscale images using the univariate and bivariate Cauchy members of the alpha-stable family. A multiplicative multichannel watermark detector is also designed for color images using the multivariate Cauchy distribution. Simulation results demonstrate not only the effectiveness of the proposed image watermarking schemes in terms of the invisibility of the watermark, but also the superiority of the watermark detectors in providing detection rates higher than that of the state-of-the-art schemes even for the watermarked images undergone various kinds of attacks

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Reversible and imperceptible watermarking approach for ensuring the integrity and authenticity of brain MR images

The digital medical workflow has many circumstances in which the image data can be manipulated both within the secured Hospital Information Systems (HIS) and outside, as images are viewed, extracted and exchanged. This potentially grows ethical and legal concerns regarding modifying images details that are crucial in medical examinations. Digital watermarking is recognised as a robust technique for enhancing trust within medical imaging by detecting alterations applied to medical images. Despite its efficiency, digital watermarking has not been widely used in medical imaging. Existing watermarking approaches often suffer from validation of their appropriateness to medical domains. Particularly, several research gaps have been identified: (i) essential requirements for the watermarking of medical images are not well defined; (ii) no standard approach can be found in the literature to evaluate the imperceptibility of watermarked images; and (iii) no study has been conducted before to test digital watermarking in a medical imaging workflow. This research aims to investigate digital watermarking to designing, analysing and applying it to medical images to confirm manipulations can be detected and tracked. In addressing these gaps, a number of original contributions have been presented. A new reversible and imperceptible watermarking approach is presented to detect manipulations of brain Magnetic Resonance (MR) images based on Difference Expansion (DE) technique. Experimental results show that the proposed method, whilst fully reversible, can also realise a watermarked image with low degradation for reasonable and controllable embedding capacity. This is fulfilled by encoding the data into smooth regions (blocks that have least differences between their pixels values) inside the Region of Interest (ROI) part of medical images and also through the elimination of the large location map (location of pixels used for encoding the data) required at extraction to retrieve the encoded data. This compares favourably to outcomes reported under current state-of-art techniques in terms of visual image quality of watermarked images. This was also evaluated through conducting a novel visual assessment based on relative Visual Grading Analysis (relative VGA) to define a perceptual threshold in which modifications become noticeable to radiographers. The proposed approach is then integrated into medical systems to verify its validity and applicability in a real application scenario of medical imaging where medical images are generated, exchanged and archived. This enhanced security measure, therefore, enables the detection of image manipulations, by an imperceptible and reversible watermarking approach, that may establish increased trust in the digital medical imaging workflow