WCAM: secured video surveillance with digital rights management

The WCAM project aims to provide an integrated system for secure delivery of video surveillance data over a wireless network, while remaining scalable and robust to transmission errors. To achieve these goals., the content is encoded in Motion-JPEG2000 and streamed with a specific RTP protocol encapsulation to prevent the loss of packets containing the most essential data. Protection of the video data is performed at content level using the standardized JPSEC syntax along with flexible encryption of quality layers or resolution levels. This selective encryption respects the JPEG2000 structure of the stream, not only ensuring end-to-end ciphered delivery, but also enabling dynamic content adaptation within the wireless network (quality of service, adaptation to the user's terminal). A DRM (Digital Rights Management) solution, called OpenSDRM is added to manage all authenticated peers on the WLAN (from end-users to cameras), as well as to manage the rights to access and display conditionally the video data. This whole integrated architecture addresses several security problems such as data encryption, integrity, access control and rights management. Using several protection lavers, the level of confidentiality can depend both on content characteristics and user rights, thus also addressing the critical issue of privacy.info:eu-repo/semantics/acceptedVersio

ROI Based Quality Access Control of Compressed Color Image using DWT via Lifting

Region-of-Interest (ROI) in an image or video signal contains important information and may be used for access control at various qualities using multiresolution analysis (MRA). This paper proposes a novel quality access control method of compressed color image by modulating the coefficients of ROI at various levels. Data modulation causes visual degradation in the original image and plays the key role in access control through reversible process. The modulation information, in the form of a secret key, is embedded in non-ROI part of the chrominance blue (Cb) channel of the color image using quantization index modulation (QIM). Lifting based DWT, rather than conventional DWT, is used to decompose the original image in order to achieve two-fold advantages, namely (1) better flexibility and low loss in image quality due to QIM and (2) better decoding reliability that leads to better access control. Only the authorized users having the full knowledge of the secret key restore the full quality of ROI. Simulation results duly support this claims

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

Towards Optimal Copyright Protection Using Neural Networks Based Digital Image Watermarking

In the field of digital watermarking, digital image watermarking for copyright protection has attracted a lot of attention in the research community. Digital watermarking contains varies techniques for protecting the digital content. Among all those techniques,Discrete Wavelet Transform (DWT) provides higher image imperceptibility and robustness. Over the years, researchers have been designing watermarking techniques with robustness in mind, in order for the watermark to be resistant against any image processing techniques. Furthermore, the requirements of a good watermarking technique includes a tradeoff between robustness, image quality (imperceptibility) and capacity. In this paper, we have done an extensive literature review for the existing DWT techniques and those combined with other techniques such as Neural Networks. In addition to that, we have discuss the contribution of Neural Networks in copyright protection. Finally we reached our goal in which we identified the research gaps existed in the current watermarking schemes. So that, it will be easily to obtain an optimal techniques to make the watermark object robust to attacks while maintaining the imperceptibility to enhance the copyright protection

WAVELET BASED DATA HIDING OF DEM IN THE CONTEXT OF REALTIME 3D VISUALIZATION (Visualisation 3D Temps-Réel à Distance de MNT par Insertion de Données Cachées Basée Ondelettes)

The use of aerial photographs, satellite images, scanned maps and digital elevation models necessitates the setting up of strategies for the storage and visualization of these data. In order to obtain a three dimensional visualization it is necessary to drape the images, called textures, onto the terrain geometry, called Digital Elevation Model (DEM). Practically, all these information are stored in three different files: DEM, texture and position/projection of the data in a geo-referential system. In this paper we propose to stock all these information in a single file for the purpose of synchronization. For this we have developed a wavelet-based embedding method for hiding the data in a colored image. The texture images containing hidden DEM data can then be sent from the server to a client in order to effect 3D visualization of terrains. The embedding method is integrable with the JPEG2000 coder to accommodate compression and multi-resolution visualization. Résumé L'utilisation de photographies aériennes, d'images satellites, de cartes scannées et de modèles numériques de terrains amène à mettre en place des stratégies de stockage et de visualisation de ces données. Afin d'obtenir une visualisation en trois dimensions, il est nécessaire de lier ces images appelées textures avec la géométrie du terrain nommée Modèle Numérique de Terrain (MNT). Ces informations sont en pratiques stockées dans trois fichiers différents : MNT, texture, position et projection des données dans un système géo-référencé. Dans cet article, nous proposons de stocker toutes ces informations dans un seul fichier afin de les synchroniser. Nous avons développé pour cela une méthode d'insertion de données cachées basée ondelettes dans une image couleur. Les images de texture contenant les données MNT cachées peuvent ensuite être envoyées du serveur au client afin d'effectuer une visualisation 3D de terrains. Afin de combiner une visualisation en multirésolution et une compression, l'insertion des données cachées est intégrable dans le codeur JPEG 2000

Secure and efficient storage of multimedia: content in public cloud environments using joint compression and encryption

The Cloud Computing is a paradigm still with many unexplored areas ranging from the technological component to the de nition of new business models, but that is revolutionizing the way we design, implement and manage the entire infrastructure of information technology. The Infrastructure as a Service is the delivery of computing infrastructure, typically a virtual data center, along with a set of APIs that allow applications, in an automatic way, can control the resources they wish to use. The choice of the service provider and how it applies to their business model may lead to higher or lower cost in the operation and maintenance of applications near the suppliers. In this sense, this work proposed to carry out a literature review on the topic of Cloud Computing, secure storage and transmission of multimedia content, using lossless compression, in public cloud environments, and implement this system by building an application that manages data in public cloud environments (dropbox and meocloud). An application was built during this dissertation that meets the objectives set. This system provides the user a wide range of functions of data management in public cloud environments, for that the user only have to login to the system with his/her credentials, after performing the login, through the Oauth 1.0 protocol (authorization protocol) is generated an access token, this token is generated only with the consent of the user and allows the application to get access to data/user les without having to use credentials. With this token the framework can now operate and unlock the full potential of its functions. With this application is also available to the user functions of compression and encryption so that user can make the most of his/her cloud storage system securely. The compression function works using the compression algorithm LZMA being only necessary for the user to choose the les to be compressed. Relatively to encryption it will be used the encryption algorithm AES (Advanced Encryption Standard) that works with a 128 bit symmetric key de ned by user. We build the research into two distinct and complementary parts: The rst part consists of the theoretical foundation and the second part is the development of computer application where the data is managed, compressed, stored, transmitted in various environments of cloud computing. The theoretical framework is organized into two chapters, chapter 2 - Background on Cloud Storage and chapter 3 - Data compression. Sought through theoretical foundation demonstrate the relevance of the research, convey some of the pertinent theories and input whenever possible, research in the area. The second part of the work was devoted to the development of the application in cloud environment. We showed how we generated the application, presented the features, advantages, and safety standards for the data. Finally, we re ect on the results, according to the theoretical framework made in the rst part and platform development. We think that the work obtained is positive and that ts the goals we set ourselves to achieve. This research has some limitations, we believe that the time for completion was scarce and the implementation of the platform could bene t from the implementation of other features.In future research it would be appropriate to continue the project expanding the capabilities of the application, test the operation with other users and make comparative tests.A Computação em nuvem é um paradigma ainda com muitas áreas por explorar que vão desde a componente tecnológica à definição de novos modelos de negócio, mas que está a revolucionar a forma como projetamos, implementamos e gerimos toda a infraestrutura da tecnologia da informação. A Infraestrutura como Serviço representa a disponibilização da infraestrutura computacional, tipicamente um datacenter virtual, juntamente com um conjunto de APls que permitirá que aplicações, de forma automática, possam controlar os recursos que pretendem utilizar_ A escolha do fornecedor de serviços e a forma como este aplica o seu modelo de negócio poderão determinar um maior ou menor custo na operacionalização e manutenção das aplicações junto dos fornecedores. Neste sentido, esta dissertação propôs· se efetuar uma revisão bibliográfica sobre a temática da Computação em nuvem, a transmissão e o armazenamento seguro de conteúdos multimédia, utilizando a compressão sem perdas, em ambientes em nuvem públicos, e implementar um sistema deste tipo através da construção de uma aplicação que faz a gestão dos dados em ambientes de nuvem pública (dropbox e meocloud). Foi construída uma aplicação no decorrer desta dissertação que vai de encontro aos objectivos definidos. Este sistema fornece ao utilizador uma variada gama de funções de gestão de dados em ambientes de nuvem pública, para isso o utilizador tem apenas que realizar o login no sistema com as suas credenciais, após a realização de login, através do protocolo Oauth 1.0 (protocolo de autorização) é gerado um token de acesso, este token só é gerado com o consentimento do utilizador e permite que a aplicação tenha acesso aos dados / ficheiros do utilizador ~em que seja necessário utilizar as credenciais. Com este token a aplicação pode agora operar e disponibilizar todo o potencial das suas funções. Com esta aplicação é também disponibilizado ao utilizador funções de compressão e encriptação de modo a que possa usufruir ao máximo do seu sistema de armazenamento cloud com segurança. A função de compressão funciona utilizando o algoritmo de compressão LZMA sendo apenas necessário que o utilizador escolha os ficheiros a comprimir. Relativamente à cifragem utilizamos o algoritmo AES (Advanced Encryption Standard) que funciona com uma chave simétrica de 128bits definida pelo utilizador. Alicerçámos a investigação em duas partes distintas e complementares: a primeira parte é composta pela fundamentação teórica e a segunda parte consiste no desenvolvimento da aplicação informática em que os dados são geridos, comprimidos, armazenados, transmitidos em vários ambientes de computação em nuvem. A fundamentação teórica encontra-se organizada em dois capítulos, o capítulo 2 - "Background on Cloud Storage" e o capítulo 3 "Data Compression", Procurámos, através da fundamentação teórica, demonstrar a pertinência da investigação. transmitir algumas das teorias pertinentes e introduzir, sempre que possível, investigações existentes na área. A segunda parte do trabalho foi dedicada ao desenvolvimento da aplicação em ambiente "cloud". Evidenciámos o modo como gerámos a aplicação, apresentámos as funcionalidades, as vantagens. Por fim, refletimos sobre os resultados , de acordo com o enquadramento teórico efetuado na primeira parte e o desenvolvimento da plataforma. Pensamos que o trabalho obtido é positivo e que se enquadra nos objetivos que nos propusemos atingir. Este trabalho de investigação apresenta algumas limitações, consideramos que o tempo para a sua execução foi escasso e a implementação da plataforma poderia beneficiar com a implementação de outras funcionalidades. Em investigações futuras seria pertinente dar continuidade ao projeto ampliando as potencialidades da aplicação, testar o funcionamento com outros utilizadores e efetuar testes comparativos.Fundação para a Ciência e a Tecnologia (FCT

Image forgery detection using textural features and deep learning

La croissance exponentielle et les progrès de la technologie ont rendu très pratique le partage de données visuelles, d'images et de données vidéo par le biais d’une vaste prépondérance de platesformes disponibles. Avec le développement rapide des technologies Internet et multimédia, l’efficacité de la gestion et du stockage, la rapidité de transmission et de partage, l'analyse en temps réel et le traitement des ressources multimédias numériques sont progressivement devenus un élément indispensable du travail et de la vie de nombreuses personnes. Sans aucun doute, une telle croissance technologique a rendu le forgeage de données visuelles relativement facile et réaliste sans laisser de traces évidentes. L'abus de ces données falsifiées peut tromper le public et répandre la désinformation parmi les masses. Compte tenu des faits mentionnés ci-dessus, la criminalistique des images doit être utilisée pour authentifier et maintenir l'intégrité des données visuelles. Pour cela, nous proposons une technique de détection passive de falsification d'images basée sur les incohérences de texture et de bruit introduites dans une image du fait de l'opération de falsification. De plus, le réseau de détection de falsification d'images (IFD-Net) proposé utilise une architecture basée sur un réseau de neurones à convolution (CNN) pour classer les images comme falsifiées ou vierges. Les motifs résiduels de texture et de bruit sont extraits des images à l'aide du motif binaire local (LBP) et du modèle Noiseprint. Les images classées comme forgées sont ensuite utilisées pour mener des expériences afin d'analyser les difficultés de localisation des pièces forgées dans ces images à l'aide de différents modèles de segmentation d'apprentissage en profondeur. Les résultats expérimentaux montrent que l'IFD-Net fonctionne comme les autres méthodes de détection de falsification d'images sur l'ensemble de données CASIA v2.0. Les résultats discutent également des raisons des difficultés de segmentation des régions forgées dans les images du jeu de données CASIA v2.0.The exponential growth and advancement of technology have made it quite convenient for people to share visual data, imagery, and video data through a vast preponderance of available platforms. With the rapid development of Internet and multimedia technologies, performing efficient storage and management, fast transmission and sharing, real-time analysis, and processing of digital media resources has gradually become an indispensable part of many people’s work and life. Undoubtedly such technological growth has made forging visual data relatively easy and realistic without leaving any obvious visual clues. Abuse of such tampered data can deceive the public and spread misinformation amongst the masses. Considering the facts mentioned above, image forensics must be used to authenticate and maintain the integrity of visual data. For this purpose, we propose a passive image forgery detection technique based on textural and noise inconsistencies introduced in an image because of the tampering operation. Moreover, the proposed Image Forgery Detection Network (IFD-Net) uses a Convolution Neural Network (CNN) based architecture to classify the images as forged or pristine. The textural and noise residual patterns are extracted from the images using Local Binary Pattern (LBP) and the Noiseprint model. The images classified as forged are then utilized to conduct experiments to analyze the difficulties in localizing the forged parts in these images using different deep learning segmentation models. Experimental results show that both the IFD-Net perform like other image forgery detection methods on the CASIA v2.0 dataset. The results also discuss the reasons behind the difficulties in segmenting the forged regions in the images of the CASIA v2.0 dataset