247 research outputs found
WG1N5315 - Response to Call for AIC evaluation methodologies and compression technologies for medical images: LAR Codec
This document presents the LAR image codec as a response to Call for AIC evaluation methodologies and compression technologies for medical images.This document describes the IETR response to the specific call for contributions of medical imaging technologies to be considered for AIC. The philosophy behind our coder is not to outperform JPEG2000 in compression; our goal is to propose an open source, royalty free, alternative image coder with integrated services. While keeping the compression performances in the same range as JPEG2000 but with lower complexity, our coder also provides services such as scalability, cryptography, data hiding, lossy to lossless compression, region of interest, free region representation and coding
An Integrated Message Hiding and Message Extraction Technique for Multimedia Content Using Invisible Watermarking Technique
The protection of multimedia data is becoming very important. The protection can be done with encryption. Involving both encryption and compression side-by-side needs more complex algorithms for content retrieval. Reconstructing the compressed encrypted content without much information loss is important. This work improves the ratio-distortion performance and also embedded message in the source image can be extracted for the source image authentication by using invisible watermarking technique. The message can be embedded into and extracted from the source image using watermarking techniques. The watermarked image is compressed by using quantization method to improve the compression ratio. The compressed image is encrypted and decrypted using modulo-256 addition by adding pseudo-random numbers into the image pixels. The encrypted image is splitted into number of files and in the user side using the auxiliary information (AI), file is merged using file adaptive wrapper method to decrypt the source image. Finally, with the use of verification key the embedded message is extracted and the source image is verified. It is shown that this method improves the ratio-distortion performance in compressing a watermarked image and better quality of reconstructed image. In order to further improve the distortion performance and quality of the reconstructed image other compression methods can be used.
DOI: 10.17762/ijritcc2321-8169.15054
Deep Joint Encryption and Source-Channel Coding: An Image Visual Protection Approach
Joint source and channel coding (JSCC) has achieved great success due to the
introduction of deep learning. Compared with traditional separate source
channel coding (SSCC) schemes, the advantages of DL based JSCC (DJSCC) include
high spectrum efficiency, high reconstruction quality, and the relief of "cliff
effect". However, it is difficult to couple encryption-decryption mechanisms
with DJSCC in contrast with traditional SSCC schemes, which hinders the
practical usage of the emerging technology. To this end, our paper proposes a
novel method called DL based joint encryption and source-channel coding
(DJESCC) for images that can successfully protect the visual information of the
plain image without significantly sacrificing image reconstruction performance.
The idea of the design is using a neural network to conduct image encryption,
which converts the plain image to a visually protected one with the
consideration of its interaction with DJSCC. During the training stage, the
proposed DJESCC method learns: 1) deep neural networks for image encryption and
image decryption, and 2) an effective DJSCC network for image transmission in
encrypted domain. Compared with the perceptual image encryption methods with
DJSCC transmission, the DJESCC method achieves much better reconstruction
performance and is more robust to ciphertext-only attacks.Comment: 12 pages, 13 figure
Discrete Wavelet Transform based Cryptosystem
In this article, the authors proposed, implemented and analysed a symmetric key cryptographic algorithm that can be considered as a lossless encryption and decryption technique, advantageous especially in situations where, even a slight marginal distortion is not tolerable. In the proposed system, Haar wavelet is used initially, to transform the original target image into its frequency domain, followed by encrypting the resulting sub-bands, so as to obtain a secure and reliable encrypted image. The resulting coefficients after Haar decomposition is scattered using a reversible weighing factor, suitably reversed and swapped to get the secure encrypted image. The encrypted image is then correspondingly decrypted, by the reverse process to get back the original decrypted image. Statistical testing and security methods were used to evaluate and analyse the proposed cryptosystem and the results showed that the proposed system is cryptographically resistant to attacks and is also highly secure when compared to other cryptographic systems in the frequency domain
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
Optimized Visual Internet of Things in Video Processing for Video Streaming
The global expansion of the Visual Internet of Things (VIoT) has enabled various new applications during the last decade through the interconnection of a wide range of devices and sensors.Frame freezing and buffering are the major artefacts in broad area of multimedia networking applications occurring due to significant packet loss and network congestion. Numerous studies have been carried out in order to understand the impact of packet loss on QoE for a wide range of applications. This paper improves the video streaming quality by using the proposed framework Lossy Video Transmission (LVT) for simulating the effect of network congestion on the performance of encrypted static images sent over wireless sensor networks.The simulations are intended for analysing video quality and determining packet drop resilience during video conversations.The assessment of emerging trends in quality measurement, including picture preference, visual attention, and audio visual quality is checked. To appropriately quantify the video quality loss caused by the encoding system, various encoders compress video sequences at various data rates.Simulation results for different QoE metrics with respect to user developed videos have been demonstrated which outperforms the existing metrics
Preserving data integrity of encoded medical images: the LAR compression framework
International audienceThrough the development of medical imaging systems and their integration into a complete information system, the need for advanced joint coding and network services becomes predominant. PACS (Picture Archiving and Communication System) aims to acquire, store and compress, retrieve, present and distribute medical images. These systems have to be accessible via the Internet or wireless channels. Thus protection processes against transmission errors have to be added to get a powerful joint source-channel coding tool. Moreover, these sensitive data require confidentiality and privacy for both archiving and transmission purposes, leading to use cryptography and data embedding solutions. This chapter introduces data integrity protection and developed dedicated tools of content protection and secure bitstream transmission for medical encoded image purposes. In particular, the LAR image coding method is defined together with advanced securization services
A Novel Latin Square Image Cipher
In this paper, we introduce a symmetric-key Latin square image cipher (LSIC)
for grayscale and color images. Our contributions to the image encryption
community include 1) we develop new Latin square image encryption primitives
including Latin Square Whitening, Latin Square S-box and Latin Square P-box ;
2) we provide a new way of integrating probabilistic encryption in image
encryption by embedding random noise in the least significant image bit-plane;
and 3) we construct LSIC with these Latin square image encryption primitives
all on one keyed Latin square in a new loom-like substitution-permutation
network. Consequently, the proposed LSIC achieve many desired properties of a
secure cipher including a large key space, high key sensitivities, uniformly
distributed ciphertext, excellent confusion and diffusion properties,
semantically secure, and robustness against channel noise. Theoretical analysis
show that the LSIC has good resistance to many attack models including
brute-force attacks, ciphertext-only attacks, known-plaintext attacks and
chosen-plaintext attacks. Experimental analysis under extensive simulation
results using the complete USC-SIPI Miscellaneous image dataset demonstrate
that LSIC outperforms or reach state of the art suggested by many peer
algorithms. All these analysis and results demonstrate that the LSIC is very
suitable for digital image encryption. Finally, we open source the LSIC MATLAB
code under webpage https://sites.google.com/site/tuftsyuewu/source-code.Comment: 26 pages, 17 figures, and 7 table
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