Data Hiding in Digital Video

With the rapid development of digital multimedia technologies, an old method which is called steganography has been sought to be a solution for data hiding applications such as digital watermarking and covert communication. Steganography is the art of secret communication using a cover signal, e.g., video, audio, image etc., whereas the counter-technique, detecting the existence of such as a channel through a statistically trained classifier, is called steganalysis. The state-of-the art data hiding algorithms utilize features; such as Discrete Cosine Transform (DCT) coefficients, pixel values, motion vectors etc., of the cover signal to convey the message to the receiver side. The goal of embedding algorithm is to maximize the number of bits sent to the decoder side (embedding capacity) with maximum robustness against attacks while keeping the perceptual and statistical distortions (security) low. Data Hiding schemes are characterized by these three conflicting requirements: security against steganalysis, robustness against channel associated and/or intentional distortions, and the capacity in terms of the embedded payload. Depending upon the application it is the designer\u27s task to find an optimum solution amongst them. The goal of this thesis is to develop a novel data hiding scheme to establish a covert channel satisfying statistical and perceptual invisibility with moderate rate capacity and robustness to combat steganalysis based detection. The idea behind the proposed method is the alteration of Video Object (VO) trajectory coordinates to convey the message to the receiver side by perturbing the centroid coordinates of the VO. Firstly, the VO is selected by the user and tracked through the frames by using a simple region based search strategy and morphological operations. After the trajectory coordinates are obtained, the perturbation of the coordinates implemented through the usage of a non-linear embedding function, such as a polar quantizer where both the magnitude and phase of the motion is used. However, the perturbations made to the motion magnitude and phase were kept small to preserve the semantic meaning of the object motion trajectory. The proposed method is well suited to the video sequences in which VOs have smooth motion trajectories. Examples of these types could be found in sports videos in which the ball is the focus of attention and exhibits various motion types, e.g., rolling on the ground, flying in the air, being possessed by a player, etc. Different sports video sequences have been tested by using the proposed method. Through the experimental results, it is shown that the proposed method achieved the goal of both statistical and perceptual invisibility with moderate rate embedding capacity under AWGN channel with varying noise variances. This achievement is important as the first step for both active and passive steganalysis is the detection of the existence of covert channel. This work has multiple contributions in the field of data hiding. Firstly, it is the first example of a data hiding method in which the trajectory of a VO is used. Secondly, this work has contributed towards improving steganographic security by providing new features: the coordinate location and semantic meaning of the object

Foveated Non-Local Means Denoising of Color Images, with Cross-Channel Paradigm.

Foveation, a peculiarity of the HVS, is characterized by a sharp image having maximal acuity at the central part of the retina, the fovea. The acuity rapidly decreases towards the periphery of the visual field. Foveated imaging was recently investigated for the purpose of image denoising in the Foveated Non-local Means (FNLM) algorithm, and it was shown that for natural images the foveated self-similarity is a far more effective regularization prior than the conventional windowed self-similarity. Color images exhibit spectral redundancy across the R, G and B channels which can be exploited to reduce the effects of noise. We extend the FNLM algorithm to the removal of additive white Gaussian noise from color images. The proposed Color-mixed Foveated NL-means algorithm, denominated as C-FNLM, implements the concept of foveated self-similarity, along with a cross-channel paradigm to exploit the correlation between color channels. The patch similarity is measured through an updated foveated distance for color images. In C-FNLM, we derive the explicit construction of an unified operator which explores the spatially variant nature of color perception in the HVS. We develop a framework for designing the linear operator that simultaneously performs foveation and color mixing. Within this framework, we construct several parametrized families of the color-mixing operation. Our analysis shows that the color-mixed foveation is a far more effective regularity assumption than the windowing conventionally used in NL-means, especially for color image denoising where substantial improvement was observed in terms of contrast and sharpness. Moreover, the unified operator is introduced at a negligible cost in terms of the computational complexity

Recent Advances in Steganography

Steganography is the art and science of communicating which hides the existence of the communication. Steganographic technologies are an important part of the future of Internet security and privacy on open systems such as the Internet. This book's focus is on a relatively new field of study in Steganography and it takes a look at this technology by introducing the readers various concepts of Steganography and Steganalysis. The book has a brief history of steganography and it surveys steganalysis methods considering their modeling techniques. Some new steganography techniques for hiding secret data in images are presented. Furthermore, steganography in speeches is reviewed, and a new approach for hiding data in speeches is introduced

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

Low delay video coding

Analogue wireless cameras have been employed for decades, however they have not become an universal solution due to their difficulties of set up and use. The main problem is the link robustness which mainly depends on the requirement of a line-of-sight view between transmitter and receiver, a working condition not always possible. Despite the use of tracking antenna system such as the Portable Intelligent Tracking Antenna (PITA [1]), if strong multipath fading occurs (e.g. obstacles between transmitter and receiver) the picture rapidly falls apart. Digital wireless cameras based on Orthogonal Frequency Division Multiplexing (OFDM) modulation schemes give a valid solution for the above problem. OFDM offers strong multipath protection due to the insertion of the guard interval; in particular, the OFDM-based DVB-T standard has proven to offer excellent performance for the broadcasting of multimedia streams with bit rates over 10 Mbps in difficult terrestrial propagation channels, for fixed and portable applications. However, in typical conditions, the latency needed to compress/decompress a digital video signal at Standard Definition (SD) resolution is of the order of 15 frames, which corresponds to ≃ 0.5 sec. This delay introduces a serious problem when wireless and wired cameras have to be interfaced. Cabled cameras do not use compression, because the cable which directly links transmitter and receiver does not impose restrictive bandwidth constraints. Therefore, the only latency that affects a cable cameras link system is the on cable propagation delay, almost not significant, when switching between wired and wireless cameras, the residual latency makes it impossible to achieve the audio-video synchronization, with consequent disagreeable effects. A way to solve this problem is to provide a low delay digital processing scheme based on a video coding algorithm which avoids massive intermediate data storage. The analysis of the last MPEG based coding standards puts in evidence a series of problems which limits the real performance of a low delay MPEG coding system. The first effort of this work is to study the MPEG standard to understand its limit from both the coding delay and implementation complexity points of views. This thesis also investigates an alternative solution based on HERMES codec, a proprietary algorithm which is described implemented and evaluated. HERMES achieves better results than MPEG in terms of latency and implementation complexity, at the price of higher compression ratios, which means high output bit rates. The use of HERMES codec together with an enhanced OFDM system [2] leads to a competitive solution for wireless digital professional video applications

Data Compression in Ultrasound Computed Tomography

The large amount of data in the Karlsruhe 3D Ultrasound Computed Tomography (USCT) has to be reduced. For compression of ultrasound signals, cascading bit-wise run length method and adjacent A-scans or samples based method as new lossless methods were developed. Lossy compression methods are evaluated with an image quality based scheme using the newly designed optical flow based and committee model based estimators. Finally, an optimal method with a feasible compression ratio was suggested

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

Context-aware Facial Inpainting with GANs

Facial inpainting is a difficult problem due to the complex structural patterns of a face image. Using irregular hole masks to generate contextualised features in a face image is becoming increasingly important in image inpainting. Existing methods generate images using deep learning models, but aberrations persist. The reason for this is that key operations are required for feature information dissemination, such as feature extraction mechanisms, feature propagation, and feature regularizers, are frequently overlooked or ignored during the design stage. A comprehensive review is conducted to examine existing methods and identify the research gaps that serve as the foundation for this thesis. The aim of this thesis is to develop novel facial inpainting algorithms with the capability of extracting contextualised features. First, Symmetric Skip Connection Wasserstein GAN (SWGAN) is proposed to inpaint high-resolution face images that are perceptually consistent with the rest of the image. Second, a perceptual adversarial Network (RMNet) is proposed to include feature extraction and feature propagation mechanisms that target missing regions while preserving visible ones. Third, a foreground-guided facial inpainting method is proposed with occlusion reasoning capability, which guides the model toward learning contextualised feature extraction and propagation while maintaining fidelity. Fourth, V-LinkNet is pro-posed that takes into account of the critical operations for information dissemination. Additionally, a standard protocol is introduced to prevent potential biases in performance evaluation of facial inpainting algorithms. The experimental results show V-LinkNet achieved the best results with SSIM of 0.96 on the standard protocol. In conclusion, generating facial images with contextualised features is important to achieve realistic results in inpainted regions. Additionally, it is critical to consider the standard procedure while comparing different approaches. Finally, this thesis outlines the new insights and future directions of image inpainting