Robust digital image watermarking

This research presents a novel rank based image watermarking method and improved moment based and histogram based image watermarking methods. A high-frequency component modification step is also proposed to compensate the side effect of commonly used Gaussian pre-filtering. The proposed methods outperform the latest image watermarking methods

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

Registration and categorization of camera captured documents

Camera captured document image analysis concerns with processing of documents captured with hand-held sensors, smart phones, or other capturing devices using advanced image processing, computer vision, pattern recognition, and machine learning techniques. As there is no constrained capturing in the real world, the captured documents suffer from illumination variation, viewpoint variation, highly variable scale/resolution, background clutter, occlusion, and non-rigid deformations e.g., folds and crumples. Document registration is a problem where the image of a template document whose layout is known is registered with a test document image. Literature in camera captured document mosaicing addressed the registration of captured documents with the assumption of considerable amount of single chunk overlapping content. These methods cannot be directly applied to registration of forms, bills, and other commercial documents where the fixed content is distributed into tiny portions across the document. On the other hand, most of the existing document image registration methods work with scanned documents under affine transformation. Literature in document image retrieval addressed categorization of documents based on text, figures, etc. However, the scalability of existing document categorization methodologies based on logo identification is very limited. This dissertation focuses on two problems (i) registration of captured documents where the overlapping content is distributed into tiny portions across the documents and (ii) categorization of captured documents into predefined logo classes that scale to large datasets using local invariant features. A novel methodology is proposed for the registration of user defined Regions Of Interest (ROI) using corresponding local features from their neighborhood. The methodology enhances prior approaches in point pattern based registration, like RANdom SAmple Consensus (RANSAC) and Thin Plate Spline-Robust Point Matching (TPS-RPM), to enable registration of cell phone and camera captured documents under non-rigid transformations. Three novel aspects are embedded into the methodology: (i) histogram based uniformly transformed correspondence estimation, (ii) clustering of points located near the ROI to select only close by regions for matching, and (iii) validation of the registration in RANSAC and TPS-RPM algorithms. Experimental results on a dataset of 480 images captured using iPhone 3GS and Logitech webcam Pro 9000 have shown an average registration accuracy of 92.75% using Scale Invariant Feature Transform (SIFT). Robust local features for logo identification are determined empirically by comparisons among SIFT, Speeded-Up Robust Features (SURF), Hessian-Affine, Harris-Affine, and Maximally Stable Extremal Regions (MSER). Two different matching methods are presented for categorization: matching all features extracted from the query document as a single set and a segment-wise matching of query document features using segmentation achieved by grouping area under intersecting dense local affine covariant regions. The later approach not only gives an approximate location of predicted logo classes in the query document but also helps to increase the prediction accuracies. In order to facilitate scalability to large data sets, inverted indexing of logo class features has been incorporated in both approaches. Experimental results on a dataset of real camera captured documents have shown a peak 13.25% increase in the F–measure accuracy using the later approach as compared to the former

Visual Servoing

The goal of this book is to introduce the visional application by excellent researchers in the world currently and offer the knowledge that can also be applied to another field widely. This book collects the main studies about machine vision currently in the world, and has a powerful persuasion in the applications employed in the machine vision. The contents, which demonstrate that the machine vision theory, are realized in different field. For the beginner, it is easy to understand the development in the vision servoing. For engineer, professor and researcher, they can study and learn the chapters, and then employ another application method

Detecting targeted data poisoning attacks on deep neural networks

Deep neural networks (DNNs) are widely used for various facial image-recognition purposes, including facial recognition and subsequent authentication, and the detection of altered facial images. Unfortunately, due to their widespread use, there have been many works that focus on attacking such DNN-based systems for nefarious purposes. One type of attack on DNNs is called a "targeted data poisoning" attack, which has the goal of injecting photos into the DNNs training set in such a way as to cause the DNN to learn malicious behavior. In the context of facial authentication, this could correspond to unauthorized users gaining access to a target's account, whereas, in deepfake detection, this could translate to causing the DNN to fail to identify when a target's face is the subject of a deepfake image. This report describes targeted data poisoning attacks and proposed defenses on DNN-based systems for facial authentication and deepfake detection, each achieving high accuracy ([greater than] 95 percent) in most cases.Includes bibliographical references

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

Framework for Automatic Identification of Paper Watermarks with Chain Codes

Title from PDF of title page viewed May 21, 2018Dissertation advisor: Reza DerakhshaniVitaIncludes bibliographical references (pages 220-235)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2017In this dissertation, I present a new framework for automated description, archiving, and identification of paper watermarks found in historical documents and manuscripts. The early manufacturers of paper have introduced the embedding of identifying marks and patterns as a sign of a distinct origin and perhaps as a signature of quality. Thousands of watermarks have been studied, classified, and archived. Most of the classification categories are based on image similarity and are searchable based on a set of defined contextual descriptors. The novel method presented here is for automatic classification, identification (matching) and retrieval of watermark images based on chain code descriptors (CC). The approach for generation of unique CC includes a novel image preprocessing method to provide a solution for rotation and scale invariant representation of watermarks. The unique codes are truly reversible, providing high ratio lossless compression, fast searching, and image matching. The development of a novel distance measure for CC comparison is also presented. Examples for the complete process are given using the recently acquired watermarks digitized with hyper-spectral imaging of Summa Theologica, the work of Antonino Pierozzi (1389 – 1459). The performance of the algorithm on large datasets is demonstrated using watermarks datasets from well-known library catalogue collections.Introduction -- Paper and paper watermarks -- Automatic identification of paper watermarks -- Rotation, Scale and translation invariant chain code -- Comparison of RST_Invariant chain code -- Automatic identification of watermarks with chain codes -- Watermark composite feature vector -- Summary -- Appendix A. Watermarks from the Bernstein Collection used in this study -- Appendix B. The original and transformed images of watermarks -- Appendix C. The transformed and scaled images of watermarks -- Appendix D. Example of chain cod

Time-resolved dosimetry for validation of 4D dose calculation in PBS proton therapy

Four-dimensional dose calculation (4D-DC) is crucial for predicting the dosimetric outcome in the presence of intra-fractional organ motion. Time-resolved dosimetry can provide significant insights into 4D pencil beam scanning dose accumulation and is therefore irreplaceable for benchmarking 4D-DC. In this study a novel approach of time-resolved dosimetry using five PinPoint ionization chambers (ICs) embedded in an anthropomorphic dynamic phantom was employed and validated against beam delivery details. Beam intensity variations as well as the beam delivery time structure were well reflected with an accuracy comparable to the temporal resolution of the IC measurements. The 4D dosimetry approach was further applied for benchmarking the 4D-DC implemented in the RayStation 6.99 treatment planning system. Agreement between computed values and measurements was investigated for (i) partial doses based on individual breathing phases, and (ii) temporally distributed cumulative doses. For varied beam delivery and patient-related parameters the average unsigned dose difference for (i) was 0.04 +/- 0.03 Gy over all considered IC measurement values, while the prescribed physical dose was 2 Gy. By implementing (ii), a strong effect of the dose gradient on measurement accuracy was observed. The gradient originated from scanned beam energy modulation and target motion transversal to the beam. Excluding measurements in the high gradient the relative dose difference between measurements and 4D-DCs for a given treatment plan at the end of delivery was 3.5% on average and 6.6% at maximum over measurement points inside the target. Overall, the agreement between 4D dose measurements in the moving phantom and retrospective 4D-DC was found to be comparable to the static dose differences for all delivery scenarios. The presented 4D-DC has been proven to be suitable for simulating treatment deliveries with various beam- as well as patient-specific parameters and can therefore be employed for dosimetric validation of different motion mitigation techniques

Security of Forensic Techniques for Digital Images

Digital images are used everywhere in modern life and mostly replace traditional photographs. At the same time, due to the popularity of image editing tools, digital images can be altered, often leaving no obvious evidence. Thus, evaluating image authenticity is indispensable. Image forensic techniques are used to detect forgeries in digital images in the absence of embedded watermarks or signatures. Nevertheless, some legitimate or illegitimate image post-processing operations can affect the quality of the forensic results. Therefore, the reliability of forensic techniques needs to be investigated. The reliability is understood in this case as the robustness against image post-processing operations or the security against deliberated attacks. In this work, we first develop a general test framework, which is used to assess the effectiveness and security of image forensic techniques under common conditions. We design different evaluation metrics, image datasets, and several different image post-processing operations as a part of the framework. Secondly, we build several image forensic tools based on selected algorithms for detecting copy-move forgeries, re-sampling artifacts, and manipulations in JPEG images. The effectiveness and robustness of the tools are evaluated by using the developed test framework. Thirdly, for each selected technique, we develop several targeted attacks. The aim of targeted attacks against a forensic technique is to remove forensic evidence present in forged images. Subsequently, by using the test framework and the targeted attacks, we can thoroughly evaluate the security of the forensic technique. We show that image forensic techniques are often sensitive and can be defeated when their algorithms are publicly known. Finally, we develop new forensic techniques which achieve higher security in comparison with state-of-the-art forensic techniques