Steganography For Embedding Data In Digital Image

The growth of the World Wide Web (WWW) has enabled the personal computer to be used as a general communications tool. As in the case of other forms of communication there is a wish for security and privacy. With literally millions of images moving on the Internet each year, it is safe to say that digital image Steganography is of real concern to many in the IT security field. Digital images could be used for a number of different types of security fear. In the business world, the sending of a harmless looking bitmap file could actually hide the latest company secrets. Steganography (literally, covered writing) is concealing of a secret message within another seemingly innocuous message, or carrier. Digital carriers include email, audio, and images. Steganography, like cryptography, is a means of providing secrecy. Steganography does so by hiding the very existence of the communication, while cryptography does so by scrambling a message so it cannot be understood. A cryptography message can be intercepted by an eavesdropper, but the eavesdropper may not even know the existence of a steganographic message. This thesis discusses the issues regarding Steganography and its application to multimedia security and communication, addressing both theoretical and practical aspects, and tackling both design and attack problems. In the fundamental part, we identify a few key elements of Steganography through a layered structure. Data hiding is concerned to be as a communication problem where the embedded data is the signal to be transmitted. The tradeoff for two major categories of embedding data using spatial domain and frequency domain will be discussed. In addition, we have found that unevenly distributed embedding capacity brings difficulty in data hiding. We propose a complete solution to this problem, addressing considerations for choosing constant or variable embedding rate and enhancing the performance for each case. In the design part, we present new data hiding algorithms for binary images, grayscale and color images, covering such applications as annotation, fingerprinting, and ownership protection

Using Dynamic Covalent Chemistry In Crosslinked Glassy Polymers To Achieve Stress Relaxation and Improve Material Performance

Covalent adaptable network (CAN) has attracted significant interest in the soft matter community, as it enables stress relaxation in a crosslinked polymer network. However, all CAN systems have developed thus far are in the rubbery state. This thesis is focused on the development of mechanically strong, high glass transition temperature (Tg) polymer networks that are capable of dynamic bond exchange at ambient temperature while maintaining superior mechanical properties. This goal was achieved by introducing moieties capable of dynamic covalent chemistry (DCC) in the resin matrix and at the resin-filler interface of thermosetting polymers to relax internal and applied stresses at the glassy state. The first part of this thesis covers the implementation of reversible addition fragmentation transfer (RAFT) into the resin phase of glassy copper(I)-catalyzed azide-alkyne cycloaddition (CuAAC) polymer networks. Correspondingly, various azide and alkyne monomers containing structurally variable allyl sulfide (AS) or trithiocarbonate (TTC) moieties were designed and synthesized following a scalable synthetic strategy. While CuAAC reactions as a highly efficient and orthogonal &ldquo;click&rdquo; chemistry yields glassy polymers composed of rigid triazole linkages with enhanced stiffness and toughness, the RAFT moieties independently undergo bond exchange leading to glassy state stress relaxation upon light exposure. This capability of glassy state stress relaxation enables significant enhancement in toughness and ductility, light-activated shape reconfiguration and photoreversal of physical aging. The second part of this thesis covers the development of adaptive interface (AI) platform by introducing moieties capable of RAFT and thiol-thioester exchange (TTE) at the resin-filler interface of a highly crosslinked, glassy thiol-ene polymer network to promote interfacial stress relaxation in the glassy state and explore the resulting evolution of composite performance. Employing this active, bond-exchanging interface overcomes the limitation of interfacial stress concentration in polymer composites that incorporate filler materials of different modulus relative to the resin phase and imparts significant benefits to the composite performance including improvements in toughness and polymerization shrinkage stress. This improvement in mechanical performance that results from the successful relaxation of interfacial stresses is a platform technology that will broadly impact the field of polymeric composites.</p

Enhancing Fuzzy Associative Rule Mining Approaches for Improving Prediction Accuracy. Integration of Fuzzy Clustering, Apriori and Multiple Support Approaches to Develop an Associative Classification Rule Base

Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. This thesis focuses on building and enhancing a generic predictive model for estimating a future value by extracting association rules (knowledge) from a quantitative database. This model is applied to several data sets obtained from different benchmark problems, and the results are evaluated through extensive experimental tests. The thesis presents an incremental development process for the prediction model with three stages. Firstly, a Knowledge Discovery (KD) model is proposed by integrating Fuzzy C-Means (FCM) with Apriori approach to extract Fuzzy Association Rules (FARs) from a database for building a Knowledge Base (KB) to predict a future value. The KD model has been tested with two road-traffic data sets. Secondly, the initial model has been further developed by including a diversification method in order to improve a reliable FARs to find out the best and representative rules. The resulting Diverse Fuzzy Rule Base (DFRB) maintains high quality and diverse FARs offering a more reliable and generic model. The model uses FCM to transform quantitative data into fuzzy ones, while a Multiple Support Apriori (MSapriori) algorithm is adapted to extract the FARs from fuzzy data. The correlation values for these FARs are calculated, and an efficient orientation for filtering FARs is performed as a post-processing method. The FARs diversity is maintained through the clustering of FARs, based on the concept of the sharing function technique used in multi-objectives optimization. The best and the most diverse FARs are obtained as the DFRB to utilise within the Fuzzy Inference System (FIS) for prediction. The third stage of development proposes a hybrid prediction model called Fuzzy Associative Classification Rule Mining (FACRM) model. This model integrates the ii improved Gustafson-Kessel (G-K) algorithm, the proposed Fuzzy Associative Classification Rules (FACR) algorithm and the proposed diversification method. The improved G-K algorithm transforms quantitative data into fuzzy data, while the FACR generate significant rules (Fuzzy Classification Association Rules (FCARs)) by employing the improved multiple support threshold, associative classification and vertical scanning format approaches. These FCARs are then filtered by calculating the correlation value and the distance between them. The advantage of the proposed FACRM model is to build a generalized prediction model, able to deal with different application domains. The validation of the FACRM model is conducted using different benchmark data sets from the University of California, Irvine (UCI) of machine learning and KEEL (Knowledge Extraction based on Evolutionary Learning) repositories, and the results of the proposed FACRM are also compared with other existing prediction models. The experimental results show that the error rate and generalization performance of the proposed model is better in the majority of data sets with respect to the commonly used models. A new method for feature selection entitled Weighting Feature Selection (WFS) is also proposed. The WFS method aims to improve the performance of FACRM model. The prediction performance is improved by minimizing the prediction error and reducing the number of generated rules. The prediction results of FACRM by employing WFS have been compared with that of FACRM and Stepwise Regression (SR) models for different data sets. The performance analysis and comparative study show that the proposed prediction model provides an effective approach that can be used within a decision support system.Applied Science University (ASU) of Jorda

Video deepfake detection using Particle Swarm Optimization improved deep neural networks

As complexity and capabilities of Artificial Intelligence technologies increase, so does its potential for misuse. Deepfake videos are an example. They are created with generative models which produce media that replicates the voices and faces of real people. Deepfake videos may be entertaining, but they may also put privacy and security at risk. A criminal may forge a video of a politician or another notable person in order to affect public opinions or deceive others. Approaches for detecting and protecting against these types of forgery must evolve as well as the methods of generation to ensure that proper information is supplied and to mitigate the risks associated with the fast evolution of deepfakes. This research exploits the effectiveness of deepfake detection algorithms with the application of a Particle Swarm Optimization (PSO) variant for hyperparameter selection. Since Convolutional Neural Networks excel in recognizing objects and patterns in visual data while Recurrent Neural Networks are proficient at handling sequential data, in this research, we propose a hybrid EfficientNet-Gated Recurrent Unit (GRU) network as well as EfficientNet-B0-based transfer learning for video forgery classification. A new PSO algorithm is proposed for hyperparameter search, which incorporates composite leaders and reinforcement learning-based search strategy allocation to mitigate premature convergence. To assess whether an image or a video is manipulated, both models are trained on datasets containing deepfake and genuine photographs and videos. The empirical results indicate that the proposed PSO-based EfficientNet-GRU and EfficientNet-B0 networks outperform the counterparts with manual and optimal learning configurations yielded by other search methods for several deepfake datasets

Cadmium Tissue Concentrations in Kidney, Liver and Muscle in Moose (Alces alces) From First Nations Communities in Northern Alberta

The consumption of traditional foods, including moose, is vitally important to Canada's indigenous communities for dietary, social, and cultural reasons. Cadmium is a key contaminant of concern in moose as it accumulates primarily the organs, with the kidney accumulating more than the liver. The objectives of this study were to identify relationships between cadmium concentrations in the kidney, liver and muscle tissue of moose, and to estimate benchmark consumption quantities that are associated with minimal health risk for three First Nation communities: the Chipewyan Prairie Déné First Nation, the Swan River First Nation and Cold Lake First Nations. Moose quality studies were conducted with the Chipewyan Prairie Déné First Nation in 2012, the Swan River First Nation in 2014 and the Cold Lake First Nations in 2016, all located in Alberta, Canada. The measured cadmium tissue concentrations from these studies were found to be comparable to those reported in the 2016 Alberta First Nations Food, Nutrition and Environment Study, and other North American studies. The results of our study suggest that linear relationships exist between cadmium concentrations in kidney and liver tissue, which can be used as a tool to predict organ concentrations in moose from northern Alberta. First Nations communities can use this information to predict cadmium tissue concentrations in both kidney and liver in the absence of actual, measured cadmium concentrations. Benchmark consumption quantities that are associated with minimal risk were estimated for the different tissue types

A systematic review of grandparents’ influence on grandchildren’s cancer risk factors

Many lifestyle patterns are established when children are young. Research has focused on the potential role of parents as a risk factor for non communicable disease in children, but there is limited investigation of the role of other caregivers, such as grandparents. The aim of this review was to identify and synthesise evidence for any influence grandparents&rsquo; care practices may have on their grandchildren&rsquo;s long term cancer risk factors. A systematic review was carried out with searches across four databases (MEDLINE, Embase, Web of Science, PsycINFO) as well as searches of reference lists and citing articles, and Google Scholar. Search terms were based on six areas of risk that family care could potentially influence&ndash;weight, diet, physical activity, tobacco, alcohol and sun exposure. All study designs were included, as were studies that provided an indication of the interaction of grandparents with their grandchildren. Studies were excluded if grandparents were primary caregivers and if children had serious health conditions. Study quality was assessed using National Institute for Health and Care Excellence checklists. Grandparent impact was categorised as beneficial, adverse, mixed or as having no impact. Due to study heterogeneity a meta-analysis was not possible. Qualitative studies underwent a thematic synthesis of their results. Results from all included studies indicated that there was a sufficient evidence base for weight, diet, physical activity and tobacco studies to draw conclusions about grandparents&rsquo; influence. One study examined alcohol and no studies examined sun exposure. Evidence indicated that, overall, grandparents had an adverse impact on their grandchildren&rsquo;s cancer risk factors. The theoretical work in the included studies was limited. Theoretically underpinned interventions designed to reduce these risk factors must consider grandparents&rsquo; role, as well as parents&rsquo;, and be evaluated robustly to inform the evidence base further

Global overview of the management of acute cholecystitis during the COVID-19 pandemic (CHOLECOVID study)

Background: This study provides a global overview of the management of patients with acute cholecystitis during the initial phase of the COVID-19 pandemic. Methods: CHOLECOVID is an international, multicentre, observational comparative study of patients admitted to hospital with acute cholecystitis during the COVID-19 pandemic. Data on management were collected for a 2-month study interval coincident with the WHO declaration of the SARS-CoV-2 pandemic and compared with an equivalent pre-pandemic time interval. Mediation analysis examined the influence of SARS-COV-2 infection on 30-day mortality. Results: This study collected data on 9783 patients with acute cholecystitis admitted to 247 hospitals across the world. The pandemic was associated with reduced availability of surgical workforce and operating facilities globally, a significant shift to worse severity of disease, and increased use of conservative management. There was a reduction (both absolute and proportionate) in the number of patients undergoing cholecystectomy from 3095 patients (56.2 per cent) pre-pandemic to 1998 patients (46.2 per cent) during the pandemic but there was no difference in 30-day all-cause mortality after cholecystectomy comparing the pre-pandemic interval with the pandemic (13 patients (0.4 per cent) pre-pandemic to 13 patients (0.6 per cent) pandemic; P = 0.355). In mediation analysis, an admission with acute cholecystitis during the pandemic was associated with a non-significant increased risk of death (OR 1.29, 95 per cent c.i. 0.93 to 1.79, P = 0.121). Conclusion: CHOLECOVID provides a unique overview of the treatment of patients with cholecystitis across the globe during the first months of the SARS-CoV-2 pandemic. The study highlights the need for system resilience in retention of elective surgical activity. Cholecystectomy was associated with a low risk of mortality and deferral of treatment results in an increase in avoidable morbidity that represents the non-COVID cost of this pandemic