A Data Hiding Method Based on Partition Variable Block Size with Exclusive-or Operation on Binary Image

In this paper, we propose a high capacity data hiding method applying in binary images. Since a binary image has only two colors, black or white, it is hard to hide data imperceptible. The capacities and imperception are always in a trade-off problem. Before embedding we shuffle the secret data by a pseudo-random number generator to keep more secure. We divide the host image into several non-overlapping (2n+1) by (2n+1) sub-blocks in an M by N host image as many as possible, where n can equal 1, 2, 3 , …, or min(M,N). Then we partition each sub-block into four overlapping (n+1) by (n+1) sub-blocks. We skip the all blacks or all whites in each (2n+1) by (2n+1) sub-blocks. We consider all four (n+1) by (n+1) sub-blocks to check the XOR between the non overlapping parts and center pixel of the (2n+1) by (2n+1) sub-block, it embed n 2 bits in each (n+1) by (n+1) sub-block, totally are 4*n 2 . The entire host image can be embedded 4×n 2×M/(2n+1)×N/(2n+1) bits. The extraction way is simply to test the XOR between center pixel with their non-overlapping part of each sub-block. All embedding bits are collected and shuffled back to the original order. The adaptive means the partitioning sub-block may affect the capacities and imperception that we want to select. The experimental results show that the method provides the large embedding capacity and keeps imperceptible and reveal the host image lossless

System Steganalysis: Implementation Vulnerabilities and Side-Channel Attacks Against Digital Steganography Systems

Steganography is the process of hiding information in plain sight, it is a technology that can be used to hide data and facilitate secret communications. Steganography is commonly seen in the digital domain where the pervasive nature of media content (image, audio, video) provides an ideal avenue for hiding secret information. In recent years, video steganography has shown to be a highly suitable alternative to image and audio steganography due to its potential advantages (capacity, flexibility, popularity). An increased interest towards research in video steganography has led to the development of video stego-systems that are now available to the public. Many of these stego-systems have not yet been subjected to analysis or evaluation, and their capabilities for performing secure, practical, and effective video steganography are unknown. This thesis presents a comprehensive analysis of the state-of-the-art in practical video steganography. Video-based stego-systems are identified and examined using steganalytic techniques (system steganalysis) to determine the security practices of relevant stego-systems. The research in this thesis is conducted through a series of case studies that aim to provide novel insights in the field of steganalysis and its capabilities towards practical video steganography. The results of this work demonstrate the impact of system attacks over the practical state-of-the-art in video steganography. Through this research, it is evident that video-based stego-systems are highly vulnerable and fail to follow many of the well-understood security practices in the field. Consequently, it is possible to confidently detect each stego-system with a high rate of accuracy. As a result of this research, it is clear that current work in practical video steganography demonstrates a failure to address key principles and best practices in the field. Continued efforts to address this will provide safe and secure steganographic technologies

Classifiers and machine learning techniques for image processing and computer vision

Orientador: Siome Klein GoldensteinTese (doutorado) - Universidade Estadual de Campinas, Instituto da ComputaçãoResumo: Neste trabalho de doutorado, propomos a utilizaçãoo de classificadores e técnicas de aprendizado de maquina para extrair informações relevantes de um conjunto de dados (e.g., imagens) para solução de alguns problemas em Processamento de Imagens e Visão Computacional. Os problemas de nosso interesse são: categorização de imagens em duas ou mais classes, detecçãao de mensagens escondidas, distinção entre imagens digitalmente adulteradas e imagens naturais, autenticação, multi-classificação, entre outros. Inicialmente, apresentamos uma revisão comparativa e crítica do estado da arte em análise forense de imagens e detecção de mensagens escondidas em imagens. Nosso objetivo é mostrar as potencialidades das técnicas existentes e, mais importante, apontar suas limitações. Com esse estudo, mostramos que boa parte dos problemas nessa área apontam para dois pontos em comum: a seleção de características e as técnicas de aprendizado a serem utilizadas. Nesse estudo, também discutimos questões legais associadas a análise forense de imagens como, por exemplo, o uso de fotografias digitais por criminosos. Em seguida, introduzimos uma técnica para análise forense de imagens testada no contexto de detecção de mensagens escondidas e de classificação geral de imagens em categorias como indoors, outdoors, geradas em computador e obras de arte. Ao estudarmos esse problema de multi-classificação, surgem algumas questões: como resolver um problema multi-classe de modo a poder combinar, por exemplo, caracteríisticas de classificação de imagens baseadas em cor, textura, forma e silhueta, sem nos preocuparmos demasiadamente em como normalizar o vetor-comum de caracteristicas gerado? Como utilizar diversos classificadores diferentes, cada um, especializado e melhor configurado para um conjunto de caracteristicas ou classes em confusão? Nesse sentido, apresentamos, uma tecnica para fusão de classificadores e caracteristicas no cenário multi-classe através da combinação de classificadores binários. Nós validamos nossa abordagem numa aplicação real para classificação automática de frutas e legumes. Finalmente, nos deparamos com mais um problema interessante: como tornar a utilização de poderosos classificadores binarios no contexto multi-classe mais eficiente e eficaz? Assim, introduzimos uma tecnica para combinação de classificadores binarios (chamados classificadores base) para a resolução de problemas no contexto geral de multi-classificação.Abstract: In this work, we propose the use of classifiers and machine learning techniques to extract useful information from data sets (e.g., images) to solve important problems in Image Processing and Computer Vision. We are particularly interested in: two and multi-class image categorization, hidden messages detection, discrimination among natural and forged images, authentication, and multiclassification. To start with, we present a comparative survey of the state-of-the-art in digital image forensics as well as hidden messages detection. Our objective is to show the importance of the existing solutions and discuss their limitations. In this study, we show that most of these techniques strive to solve two common problems in Machine Learning: the feature selection and the classification techniques to be used. Furthermore, we discuss the legal and ethical aspects of image forensics analysis, such as, the use of digital images by criminals. We introduce a technique for image forensics analysis in the context of hidden messages detection and image classification in categories such as indoors, outdoors, computer generated, and art works. From this multi-class classification, we found some important questions: how to solve a multi-class problem in order to combine, for instance, several different features such as color, texture, shape, and silhouette without worrying about the pre-processing and normalization of the combined feature vector? How to take advantage of different classifiers, each one custom tailored to a specific set of classes in confusion? To cope with most of these problems, we present a feature and classifier fusion technique based on combinations of binary classifiers. We validate our solution with a real application for automatic produce classification. Finally, we address another interesting problem: how to combine powerful binary classifiers in the multi-class scenario more effectively? How to boost their efficiency? In this context, we present a solution that boosts the efficiency and effectiveness of multi-class from binary techniques.DoutoradoEngenharia de ComputaçãoDoutor em Ciência da Computaçã

Digital Watermarking for Verification of Perception-based Integrity of Audio Data

In certain application fields digital audio recordings contain sensitive content. Examples are historical archival material in public archives that preserve our cultural heritage, or digital evidence in the context of law enforcement and civil proceedings. Because of the powerful capabilities of modern editing tools for multimedia such material is vulnerable to doctoring of the content and forgery of its origin with malicious intent. Also inadvertent data modification and mistaken origin can be caused by human error. Hence, the credibility and provenience in terms of an unadulterated and genuine state of such audio content and the confidence about its origin are critical factors. To address this issue, this PhD thesis proposes a mechanism for verifying the integrity and authenticity of digital sound recordings. It is designed and implemented to be insensitive to common post-processing operations of the audio data that influence the subjective acoustic perception only marginally (if at all). Examples of such operations include lossy compression that maintains a high sound quality of the audio media, or lossless format conversions. It is the objective to avoid de facto false alarms that would be expectedly observable in standard crypto-based authentication protocols in the presence of these legitimate post-processing. For achieving this, a feasible combination of the techniques of digital watermarking and audio-specific hashing is investigated. At first, a suitable secret-key dependent audio hashing algorithm is developed. It incorporates and enhances so-called audio fingerprinting technology from the state of the art in contentbased audio identification. The presented algorithm (denoted as ”rMAC” message authentication code) allows ”perception-based” verification of integrity. This means classifying integrity breaches as such not before they become audible. As another objective, this rMAC is embedded and stored silently inside the audio media by means of audio watermarking technology. This approach allows maintaining the authentication code across the above-mentioned admissible post-processing operations and making it available for integrity verification at a later date. For this, an existent secret-key ependent audio watermarking algorithm is used and enhanced in this thesis work. To some extent, the dependency of the rMAC and of the watermarking processing from a secret key also allows authenticating the origin of a protected audio. To elaborate on this security aspect, this work also estimates the brute-force efforts of an adversary attacking this combined rMAC-watermarking approach. The experimental results show that the proposed method provides a good distinction and classification performance of authentic versus doctored audio content. It also allows the temporal localization of audible data modification within a protected audio file. The experimental evaluation finally provides recommendations about technical configuration settings of the combined watermarking-hashing approach. Beyond the main topic of perception-based data integrity and data authenticity for audio, this PhD work provides new general findings in the fields of audio fingerprinting and digital watermarking. The main contributions of this PhD were published and presented mainly at conferences about multimedia security. These publications were cited by a number of other authors and hence had some impact on their works

Use of the internet for information organization, distance learning, and specimen presentation

The advent of the internet has had an effect on the discipline of entomology. The history of the relationship between entomology and the internet is summarized, and several effects are examined in detail. One effect is to create an explosion of available information about insects and pest management, largely available on the world-wide web (WWW). A metadata-based solution to categorizing, searching and filtering this information is presented, along with a case study that used this solution to examine the value added by the use of metadata. In the case study website, one third of the users arrived at web pages containing entomological information by following links that were autogenerated based on metadata. Original software for extracting, assigning and managing metadata across sites is presented. A second effect is the enabling of new teaching methods, including the use of three-dimensional (3D) virtual reality insect models. Photographic 3D models were created using QuickTime VR and compared to standard teaching methodology. The QTVR models were significantly more effective. Lastly, the internet enables distance education. A web-based online introductory distance education course in entomology was constructed and offered for several years. Enrollment increased markedly over the time course was offered. Retention averaged 79% +/- 7.9% in the online section compared to 93% +/- 4.0% in the traditional section. Analysis of log files showed that problems with cheating during online evaluations was rampant, with 15 of 22 students cheating on one or both of the exams analyzed. Potentional solutions to this problem are presented.*;*This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation)

Collaborative learning utilizing a domain-based shared data repository to enhance learning outcomes

A number of learning paradigms have postulated that knowledge formation is a dynamic process where learners actively construct a representation of concepts integrating information from multiple sources. Current teaching strategies utilize a compartmentalized approach where individual courses contain a small subset of the knowledge required for a discipline. The intent of this research is to provide a framework to integrate the components of a discipline into a cohesive whole and accelerate the integration of concepts enhancing the learning process. The components utilized to accomplish these goals include two new knowledge integration models; a Knowledge Weighting Model (KWM) and the Aggregate-Integrate-Master (AIM) model. Semantic Web design principles utilizing a Resource Description Framework (RDF) schema and Web Ontology Language (OWL) will be used to define concepts and relationships for this knowledge domain that can then be extended for other domains. Lastly, a Design Research paradigm will be utilized to analyze the IT artifact, the Constructivist Unifying Baccalaureate Epistemology (CUBE) knowledge repository that was designed to validate this research. The prototype testing population utilized sixty students spanning five classes, in the fall 2007, following IRB approved protocols. Data was gathered using a Constructivist Multimedia Learning Survey (CMLES), focus groups and semi-structured interviews. This preliminary data supported the hypotheses that students using the Integrated Knowledge Repository will first; have a more positive perception of the learning process than those who use conventional single course teaching paradigms and second; students utilizing the IKR will develop a more complex understanding of the interconnected nature of the materials linking a discipline than those who take conventional single topic courses. Learning is an active process in which learners construct new ideas or concepts based upon their current/past knowledge. The goal is to develop a knowledge structure that is capable of facilitating the integration of conceptual development in a field of study

Understanding the Contemporary Value of Past Methods of Producing Theatre::Toward a Tripartite Approach to Venue - Performance - Document

This thesis evaluates the contemporary relevance of recent historical relationships between alternative theatre and its venues. It examines these relationships through what I term – following Pearson (2010) – a ‘tripartite’ approach to venue, performance and the archival documents that record them. The research engages in a practice-based methodology that aims to reconstruct such relationships using the performance history of Chapter Arts Centre, Cardiff, in the 1970s as a major case study. In Chapter One the thesis draws on literature from three distinct fields in theatre and performance studies that have each addressed different facets of the relationship between venue, performance and document: the debate on the relationship between performance and archive (Taylor 2003, Reason 2003 and 2006, Roms 2013), the discussion on re-enactment (esp. Schneider 2001 and 2011) and literature on site and “ghosting” (esp. Carlson 2003; Taylor 2003). I argue that the available literature does currently not consider sufficiently the historical role that the venue played as both a physical site and a producing facility for the performance work that happened within it. To explore further the relationship between venue-performance-document, I turn in Chapter Two to case studies of recent projects that have examined this in reference to Chapter Arts Centre and its contemporaries, Arnolfini (Bristol) and the CCA (Glasgow). Chapter Three offers an account of the performance history of Chapter Arts Centre in the 1970s, based on archival research and oral history interviews, to examine the relationship between the venue’s innovative residency programme and its visiting performance companies. Chapter Four is a reflective account of my three practice-based experiments, in which I develop and test my ‘tripartite’ approach, drawing on literature on embodied historiographic practice (Taylor 2003; 2006) and adopting a form of “generative” and “active” archive (Lepecki 2010). To conclude I reflect on the value for today of thus reaching back to former approaches and policies, suggesting that the tripartite reconstruction of the three elements of a venue’s past offers a transferable model with which to communicate a vital aspect of performance histor

Virtual Online Worlds: Towards a Collaborative Space for Architects

Although research has been trickling forth in the last eight years about online collaboration and use of virtual online worlds (VOW) amongst architects and architectural students (2006-2010), little discussion is dedicated to how the use of VOWs have improved collaboration, communication and quality of design for those that have used it. Researching VOWs and their use in architecture was a difficult task since much of what needed to be found was scattered amongst the fields of education, construction engineering, computer science and even online blogs dedicated to architecture in video games. An analysis of those findings has contributed to the development of a pilot project conducted in a VOW called Blue Mars. The project was set up in order to discover how VOWs improve communication skills of its users and analyze what happens when architecture students are allowed to virtually experience their designs as avatars. This study is part of a growing body of research on the exploration of virtual online worlds in the practice of architecture both in the classroom and out in the field