Compressive sensing based secret signals recovery for effective image steganalysis in secure communications

Conventional image steganalysis mainly focus on presence detection rather than the recovery of the original secret messages that were embedded in the host image. To address this issue, we propose an image steganalysis method featured in the compressive sensing (CS) domain, where block CS measurement matrix senses the transform coefficients of stego-image to reflect the statistical differences between the cover and stego- images. With multi-hypothesis prediction in the CS domain, the reconstruction of hidden signals is achieved efficiently. Extensive experiments have been carried out on five diverse image databases and benchmarked with four typical stegographic algorithms. The comprehensive results have demonstrated the efficacy of the proposed approach as a universal scheme for effective detection of stegography in secure communications whilst it has greatly reduced the numbers of features requested for secret signal reconstruction

ИНТЕЛЛЕКТУАЛЬНЫЙ числовым программным ДЛЯ MIMD-компьютер

For most scientific and engineering problems simulated on computers the solving of problems of the computational mathematics with approximately given initial data constitutes an intermediate or a final stage. Basic problems of the computational mathematics include the investigating and solving of linear algebraic systems, evaluating of eigenvalues and eigenvectors of matrices, the solving of systems of non-linear equations, numerical integration of initial- value problems for systems of ordinary differential equations.Для більшості наукових та інженерних задач моделювання на ЕОМ рішення задач обчислювальної математики з наближено заданими вихідними даними складає проміжний або остаточний етап. Основні проблеми обчислювальної математики відносяться дослідження і рішення лінійних алгебраїчних систем оцінки власних значень і власних векторів матриць, рішення систем нелінійних рівнянь, чисельного інтегрування початково задач для систем звичайних диференціальних рівнянь.Для большинства научных и инженерных задач моделирования на ЭВМ решение задач вычислительной математики с приближенно заданным исходным данным составляет промежуточный или окончательный этап. Основные проблемы вычислительной математики относятся исследования и решения линейных алгебраических систем оценки собственных значений и собственных векторов матриц, решение систем нелинейных уравнений, численного интегрирования начально задач для систем обыкновенных дифференциальных уравнений

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

Application and Theory of Multimedia Signal Processing Using Machine Learning or Advanced Methods

This Special Issue is a book composed by collecting documents published through peer review on the research of various advanced technologies related to applications and theories of signal processing for multimedia systems using ML or advanced methods. Multimedia signals include image, video, audio, character recognition and optimization of communication channels for networks. The specific contents included in this book are data hiding, encryption, object detection, image classification, and character recognition. Academics and colleagues who are interested in these topics will find it interesting to read

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

Optimisation of Tamper Localisation and Recovery Watermarking Techniques

Digital watermarking has found many applications in many fields, such as: copyright tracking, media authentication, tamper localisation and recovery, hardware control, and data hiding. The idea of digital watermarking is to embed arbitrary data inside a multimedia cover without affecting the perceptibility of the multimedia cover itself. The main advantage of using digital watermarking over other techniques, such as signature based techniques, is that the watermark is embedded into the multimedia cover itself and will not be removed even with the format change. Image watermarking techniques are categorised according to their robustness against modification into: fragile, semi-fragile, and robust watermarking. In fragile watermarking any change to the image will affect the watermark, this makes fragile watermarking very useful in image authentication applications, as in medical and forensic fields, where any tampering of the image is: detected, localised, and possibly recovered. Fragile watermarking techniques are also characterised by a higher capacity when compared to semi-fragile and robust watermarking. Semifragile watermarking techniques resist some modifications, such as lossy compression and low pass filtering. Semi-fragile watermarking can be used in authentication and copyright validation applications whenever the amount of embedded information is small and the expected modifications are not severe. Robust watermarking techniques are supposed to withstand more severe modifications, such as rotation and geometrical bending. Robust watermarking is used in copyright validation applications, where copyright information in the image must remains accessible even after severe modification. This research focuses on the application of image watermarking in tamper localisation and recovery and it aims to provide optimisation for some of its aspects. The optimisation aims to produce watermarking techniques that enhance one or more of the following aspects: consuming less payload, having better recovery quality, recovering larger tampered area, requiring less calculations, and being robust against the different counterfeiting attacks. Through the survey of the main existing techniques, it was found that most of them are using two separate sets of data for the localisation and the recovery of the tampered area, which is considered as a redundancy. The main focus in this research is to investigate employing image filtering techniques in order to use only one set of data for both purposes, leading to a reduced redundancy in the watermark embedding and enhanced capacity. Four tamper localisation and recovery techniques were proposed, three of them use one set of data for localisation and recovery while the fourth one is designed to be optimised and gives a better performance even though it uses separate sets of data for localisation and recovery. The four techniques were analysed and compared to two recent techniques in the literature. The performance of the proposed techniques vary from one technique to another. The fourth technique shows the best results regarding recovery quality and Probability of False Acceptance (PFA) when compared to the other proposed techniques and the two techniques in the literature, also, all proposed techniques show better recovery quality when compared to the two techniques in the literature