Data Encryption and Hashing Schemes for Multimedia Protection

There are millions of people using social networking sites like Facebook, Google+, and Youtube every single day across the entire world for sharing photos and other digital media. Unfortunately, sometimes people publish content that does not belong to them. As a result, there is an increasing demand for quality software capable of providing maximum protection for copyrighted material. In addition, confidential content such as medical images and patient records require high level of security so that they can be protected from unintended disclosure, when transferred over the Internet. On the other hand, decreasing the size of an image without significant loss in quality is always highly desirable. Hence, the need for efficient compression algorithms. This thesis introduces a robust method for image compression in the shearlet domain. Motivated by the outperformance of the Discrete Shearlet Transform (DST) compared to the Discrete Wavelet Transform (DWT) in encoding the directional information in images, we propose a DST-based compression algorithm that provides not only a better quality in terms of image approximation and compression ratio, but also increases the security of images via the Advanced Encryption Standard. Experimental results on a slew of medical images illustrate an improved performance in image quality of the proposed approximation approach in comparison to DWT, and also demonstrate its robustness against a variety of tests, including randomness, entropy, key sensitivity, and input sensitivity. We also present a 3D mesh hashing technique using spectral graph theory. The main idea is to partition a 3D model into sub-meshes, followed by the generation of the Laplace-Beltrami matrix of each sub-mesh, and the application of eigen-decomposition. This, in turn, is followed by the hashing of each sub-mesh using Tsallis entropy. The experimental results using a benchmark 3D models demonstrate the effectiveness of the proposed hashing scheme

Steganography and steganalysis: data hiding in Vorbis audio streams

The goal of the current work is to introduce ourselves in the world of steganography and steganalysis, centering our efforts in acoustic signals, a branch of steganography and steganalysis which has received much less attention than steganography and steganalysis for images. With this purpose in mind, it’s essential to get first a basic level of understanding of signal theory and the properties of the Human Auditory System, and we will dedicate ourselves to that aim during the first part of this work. Once established those basis, in the second part, we will obtain a precise image of the state of the art in steganographic and steganalytic sciences, from which we will be able to establish or deduce some good practices guides. With both previous subjects in mind, we will be able to create, design and implement a stego-system over Vorbis audio codec and, finally, as conclusion, analyze it using the principles studied during the first and second parts

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

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