Lighting and Optical Tools for Image Forensics

We present new forensic tools that are capable of detecting traces of tampering in digital images without the use of watermarks or specialized hardware. These tools operate under the assumption that images contain natural properties from a variety of sources, including the world, the lens, and the sensor. These properties may be disturbed by digital tampering and by measuring them we can expose the forgery. In this context, we present the following forensic tools: (1) illuminant direction, (2) specularity, (3) lighting environment, and (4) chromatic aberration. The common theme of these tools is that they exploit lighting or optical properties of images. Although each tool is not applicable to every image, they add to a growing set of image forensic tools that together will complicate the process of making a convincing forgery

Suppression of blocking artifact in compressed image

Image compression is actually major content for certain perspectives in the area of interactive media communication. Image processing is the mechanism for handling different kinds of images, processed images can be stored routinely and conveyance of such kind of images from one place to another place becomes simple to the user. By using image compression technique we are able to represent the image with lesser number of data bits. image compression execution can cut down the bandwidth and the volume of the data to be transmitted. (BDCT) block-based discrete cosine transform is long establish used transform for the two static and uninterrupted images. While we compress any kind of image by lossy type of image compression technique then there will be loss of data bits, we have to confrontation unwanted artifacts ringing and blocking artifacts and when we want to restore such kind of image then we face problem of blurring of images, which is sometimes called as the annoying artifacts problem near the block of the image. The recovered images from jpeg compression create blocking artifact near block boundaries of the image in high compression. Artifacts take on several forms in images. We are going to focus on blocking artifacts at medium and high level compression. Various types of images can be processed and we can diminish blocking artifacts up to tolerable level. Some standard techniques MPEG and JPEG are used in video and image processing field respectively for the compression. Lossy image compression technique is used in photographic images because loss of bits is tolerable, Since last few decades, image compression in real time applications has been a provocative field for image processing professionals. To recover original image decompression succeed by the different post processing techniques. High quality image communication with low-bit rate

Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Prioritizing Content of Interest in Multimedia Data Compression

Image and video compression techniques make data transmission and storage in digital multimedia systems more efficient and feasible for the system's limited storage and bandwidth. Many generic image and video compression techniques such as JPEG and H.264/AVC have been standardized and are now widely adopted. Despite their great success, we observe that these standard compression techniques are not the best solution for data compression in special types of multimedia systems such as microscopy videos and low-power wireless broadcast systems. In these application-specific systems where the content of interest in the multimedia data is known and well-defined, we should re-think the design of a data compression pipeline. We hypothesize that by identifying and prioritizing multimedia data's content of interest, new compression methods can be invented that are far more effective than standard techniques. In this dissertation, a set of new data compression methods based on the idea of prioritizing the content of interest has been proposed for three different kinds of multimedia systems. I will show that the key to designing efficient compression techniques in these three cases is to prioritize the content of interest in the data. The definition of the content of interest of multimedia data depends on the application. First, I show that for microscopy videos, the content of interest is defined as the spatial regions in the video frame with pixels that don't only contain noise. Keeping data in those regions with high quality and throwing out other information yields to a novel microscopy video compression technique. Second, I show that for a Bluetooth low energy beacon based system, practical multimedia data storage and transmission is possible by prioritizing content of interest. I designed custom image compression techniques that preserve edges in a binary image, or foreground regions of a color image of indoor or outdoor objects. Last, I present a new indoor Bluetooth low energy beacon based augmented reality system that integrates a 3D moving object compression method that prioritizes the content of interest.Doctor of Philosoph

Image Stitching

Projecte final de carrera fet en col.laboració amb University of Limerick. Department of Electronic and Computer EngineeringEnglish: Image processing is any form of signal processing for which the input is an image, such as a photograph or video frame; the output of image processing may be either an image or, a set of characteristics or parameters related to the image. Most image processing techniques involve treating the image as a two-dimensional signal and applying standard signal processing techniques to it. Specifically, image stitching presents different stages to render two or more overlapping images into a seamless stitched image, from the detection of features to blending in a final image. In this process, Scale Invariant Feature Transform (SIFT) algorithm can be applied to perform the detection and matching control points step, due to its good properties. The process of create an automatic and effective whole stitching process leads to analyze different methods of the stitching stages. Several commercial and online software tools are available to perform the stitching process, offering diverse options in different situations. This analysis involves the creation of a script to deal with images and project data files. Once the whole script is generated, the stitching process is able to achieve an automatic execution allowing good quality results in the final composite image.Castellano: Procesado de imagen es cualquier tipo de procesado de señal en aquel que la entrada es una imagen, como una fotografía o fotograma de video; la salida puede ser una imagen o conjunto de características y parámetros relacionados con la imagen. Muchas de las técnicas de procesado de imagen implican un tratamiento de la imagen como señal en dos dimensiones, y para ello se aplican técnicas estándar de procesado de señal. Concretamente, la costura o unión de imágenes presenta diferentes etapas para unir dos o más imágenes superpuestas en una imagen perfecta sin costuras, desde la detección de puntos clave en las imágenes hasta su mezcla en la imagen final. En este proceso, el algoritmo Scale Invariant Feature Transform (SIFT) puede ser aplicado para desarrollar la fase de detección y selección de correspondencias entre imágenes debido a sus buenas cualidades. El desarrollo de la creación de un completo proceso de costura automático y efectivo, pasa por analizar diferentes métodos de las etapas del cosido de las imágenes. Varios software comerciales y gratuitos son capaces de llevar a cabo el proceso de costura, ofreciendo diferentes alternativas en distintas situaciones. Este análisis implica la creación de una secuencia de comandos que trabaja con las imágenes y con archivos de datos del proyecto generado. Una vez esta secuencia es creada, el proceso de cosido de imágenes es capaz de lograr una ejecución automática permitiendo unos resultados de calidad en la imagen final.Català: Processament d'imatge és qualsevol tipus de processat de senyal en aquell que l'entrada és una imatge, com una fotografia o fotograma de vídeo, i la sortida pot ser una imatge o conjunt de característiques i paràmetres relacionats amb la imatge. Moltes de les tècniques de processat d'imatge impliquen un tractament de la imatge com a senyal en dues dimensions, i per això s'apliquen tècniques estàndard de processament de senyal. Concretament, la costura o unió d'imatges presenta diferents etapes per unir dues o més imatges superposades en una imatge perfecta sense costures, des de la detecció de punts clau en les imatges fins a la seva barreja en la imatge final. En aquest procés, l'algoritme Scale Invariant Feature Transform (SIFT) pot ser aplicat per desenvolupar la fase de detecció i selecció de correspondències entre imatges a causa de les seves bones qualitats. El desenvolupament de la creació d'un complet procés de costura automàtic i efectiu, passa per analitzar diferents mètodes de les etapes del cosit de les imatges. Diversos programari comercials i gratuïts són capaços de dur a terme el procés de costura, oferint diferents alternatives en diverses situacions. Aquesta anàlisi implica la creació d'una seqüència de commandes que treballa amb les imatges i amb arxius de dades del projecte generat. Un cop aquesta seqüència és creada, el procés de cosit d'imatges és capaç d'aconseguir una execució automàtica permetent uns resultats de qualitat en la imatge final

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Stereoscopic high dynamic range imaging

Two modern technologies show promise to dramatically increase immersion in virtual environments. Stereoscopic imaging captures two images representing the views of both eyes and allows for better depth perception. High dynamic range (HDR) imaging accurately represents real world lighting as opposed to traditional low dynamic range (LDR) imaging. HDR provides a better contrast and more natural looking scenes. The combination of the two technologies in order to gain advantages of both has been, until now, mostly unexplored due to the current limitations in the imaging pipeline. This thesis reviews both fields, proposes stereoscopic high dynamic range (SHDR) imaging pipeline outlining the challenges that need to be resolved to enable SHDR and focuses on capture and compression aspects of that pipeline. The problems of capturing SHDR images that would potentially require two HDR cameras and introduce ghosting, are mitigated by capturing an HDR and LDR pair and using it to generate SHDR images. A detailed user study compared four different methods of generating SHDR images. Results demonstrated that one of the methods may produce images perceptually indistinguishable from the ground truth. Insights obtained while developing static image operators guided the design of SHDR video techniques. Three methods for generating SHDR video from an HDR-LDR video pair are proposed and compared to the ground truth SHDR videos. Results showed little overall error and identified a method with the least error. Once captured, SHDR content needs to be efficiently compressed. Five SHDR compression methods that are backward compatible are presented. The proposed methods can encode SHDR content to little more than that of a traditional single LDR image (18% larger for one method) and the backward compatibility property encourages early adoption of the format. The work presented in this thesis has introduced and advanced capture and compression methods for the adoption of SHDR imaging. In general, this research paves the way for a novel field of SHDR imaging which should lead to improved and more realistic representation of captured scenes

Acquisition, compression and rendering of depth and texture for multi-view video

Three-dimensional (3D) video and imaging technologies is an emerging trend in the development of digital video systems, as we presently witness the appearance of 3D displays, coding systems, and 3D camera setups. Three-dimensional multi-view video is typically obtained from a set of synchronized cameras, which are capturing the same scene from different viewpoints. This technique especially enables applications such as freeviewpoint video or 3D-TV. Free-viewpoint video applications provide the feature to interactively select and render a virtual viewpoint of the scene. A 3D experience such as for example in 3D-TV is obtained if the data representation and display enable to distinguish the relief of the scene, i.e., the depth within the scene. With 3D-TV, the depth of the scene can be perceived using a multi-view display that renders simultaneously several views of the same scene. To render these multiple views on a remote display, an efficient transmission, and thus compression of the multi-view video is necessary. However, a major problem when dealing with multiview video is the intrinsically large amount of data to be compressed, decompressed and rendered. We aim at an efficient and flexible multi-view video system, and explore three different aspects. First, we develop an algorithm for acquiring a depth signal from a multi-view setup. Second, we present efficient 3D rendering algorithms for a multi-view signal. Third, we propose coding techniques for 3D multi-view signals, based on the use of an explicit depth signal. This motivates that the thesis is divided in three parts. The first part (Chapter 3) addresses the problem of 3D multi-view video acquisition. Multi-view video acquisition refers to the task of estimating and recording a 3D geometric description of the scene. A 3D description of the scene can be represented by a so-called depth image, which can be estimated by triangulation of the corresponding pixels in the multiple views. Initially, we focus on the problem of depth estimation using two views, and present the basic geometric model that enables the triangulation of corresponding pixels across the views. Next, we review two calculation/optimization strategies for determining corresponding pixels: a local and a one-dimensional optimization strategy. Second, to generalize from the two-view case, we introduce a simple geometric model for estimating the depth using multiple views simultaneously. Based on this geometric model, we propose a new multi-view depth-estimation technique, employing a one-dimensional optimization strategy that (1) reduces the noise level in the estimated depth images and (2) enforces consistent depth images across the views. The second part (Chapter 4) details the problem of multi-view image rendering. Multi-view image rendering refers to the process of generating synthetic images using multiple views. Two different rendering techniques are initially explored: a 3D image warping and a mesh-based rendering technique. Each of these methods has its limitations and suffers from either high computational complexity or low image rendering quality. As a consequence, we present two image-based rendering algorithms that improves the balance on the aforementioned issues. First, we derive an alternative formulation of the relief texture algorithm which was extented to the geometry of multiple views. The proposed technique features two advantages: it avoids rendering artifacts ("holes") in the synthetic image and it is suitable for execution on a standard Graphics Processor Unit (GPU). Second, we propose an inverse mapping rendering technique that allows a simple and accurate re-sampling of synthetic pixels. Experimental comparisons with 3D image warping show an improvement of rendering quality of 3.8 dB for the relief texture mapping and 3.0 dB for the inverse mapping rendering technique. The third part concentrates on the compression problem of multi-view texture and depth video (Chapters 5–7). In Chapter 5, we extend the standard H.264/MPEG-4 AVC video compression algorithm for handling the compression of multi-view video. As opposed to the Multi-view Video Coding (MVC) standard that encodes only the multi-view texture data, the proposed encoder peforms the compression of both the texture and the depth multi-view sequences. The proposed extension is based on exploiting the correlation between the multiple camera views. To this end, two different approaches for predictive coding of views have been investigated: a block-based disparity-compensated prediction technique and a View Synthesis Prediction (VSP) scheme. Whereas VSP relies on an accurate depth image, the block-based disparity-compensated prediction scheme can be performed without any geometry information. Our encoder adaptively selects the most appropriate prediction scheme using a rate-distortion criterion for an optimal prediction-mode selection. We present experimental results for several texture and depth multi-view sequences, yielding a quality improvement of up to 0.6 dB for the texture and 3.2 dB for the depth, when compared to solely performing H.264/MPEG-4AVC disparitycompensated prediction. Additionally, we discuss the trade-off between the random-access to a user-selected view and the coding efficiency. Experimental results illustrating and quantifying this trade-off are provided. In Chapter 6, we focus on the compression of a depth signal. We present a novel depth image coding algorithm which concentrates on the special characteristics of depth images: smooth regions delineated by sharp edges. The algorithm models these smooth regions using parameterized piecewiselinear functions and sharp edges by a straight line, so that it is more efficient than a conventional transform-based encoder. To optimize the quality of the coding system for a given bit rate, a special global rate-distortion optimization balances the rate against the accuracy of the signal representation. For typical bit rates, i.e., between 0.01 and 0.25 bit/pixel, experiments have revealed that the coder outperforms a standard JPEG-2000 encoder by 0.6-3.0 dB. Preliminary results were published in the Proceedings of 26th Symposium on Information Theory in the Benelux. In Chapter 7, we propose a novel joint depth-texture bit-allocation algorithm for the joint compression of texture and depth images. The described algorithm combines the depth and texture Rate-Distortion (R-D) curves, to obtain a single R-D surface that allows the optimization of the joint bit-allocation in relation to the obtained rendering quality. Experimental results show an estimated gain of 1 dB compared to a compression performed without joint bit-allocation optimization. Besides this, our joint R-D model can be readily integrated into an multi-view H.264/MPEG-4 AVC coder because it yields the optimal compression setting with a limited computation effort

Matrix Transform Imager Architecture for On-Chip Low-Power Image Processing

Camera-on-a-chip systems have tried to include carefully chosen signal processing units for better functionality, performance and also to broaden the applications they can be used for. Image processing sensors have been possible due advances in CMOS active pixel sensors (APS) and neuromorphic focal plane imagers. Some of the advantages of these systems are compact size, high speed and parallelism, low power dissipation, and dense system integration. One can envision using these chips for portable and inexpensive video cameras on hand-held devices like personal digital assistants (PDA) or cell-phones In neuromorphic modeling of the retina it would be very nice to have processing capabilities at the focal plane while retaining the density of typical APS imager designs. Unfortunately, these two goals have been mostly incompatible. We introduce our MAtrix Transform Imager Architecture (MATIA) that uses analog floating--gate devices to make it possible to have computational imagers with high pixel densities. The core imager performs computations at the pixel plane, but still has a fill-factor of 46 percent - comparable to the high fill-factors of APS imagers. The processing is performed continuously on the image via programmable matrix operations that can operate on the entire image or blocks within the image. The resulting data-flow architecture can directly perform all kinds of block matrix image transforms. Since the imager operates in the subthreshold region and thus has low power consumption, this architecture can be used as a low-power front end for any system that utilizes these computations. Various compression algorithms (e.g. JPEG), that use block matrix transforms, can be implemented using this architecture. Since MATIA can be used for gradient computations, cheap image tracking devices can be implemented using this architecture. Other applications of this architecture can range from stand-alone universal transform imager systems to systems that can compute stereoscopic depth.Ph.D.Committee Chair: Hasler, Paul; Committee Member: David Anderson; Committee Member: DeWeerth, Steve; Committee Member: Jackson, Joel; Committee Member: Smith, Mar