Spread Image
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Spread Image
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This project provides a set of tasks and scripts used to create and update images used by spread.
This documents explains the images matching criteria and shows the examples to add and update all the supported images.
The following sections explain the images matching criteria used on gce and show how to create/update images used to run the snapd test suite.
The criteria used to match images on google backend is defined by the following criteria:
The system name in the spread.yaml is used by default to match the image in gce, but the image property can be used instead as in the following example:
To select the project, when it is provided in the image property, then it is used, oterwise computeengine will be used by default and if there is not match it will retry with these projects: "ubuntu-os-cloud", "centos-cloud", "debian-cloud", "opensuse-cloud", "freebsd-org-cloud-dev".
To select the image, first it is considered exact matches on name, next is considered exact matches on family and otherwise use term matching. Terms matching method matches when all the single terms of the image name used in the spread.yaml are contained in the description of an image. See this example:
The images to be matched are ordered by creation date (latest first).
The criteria for naming images on computeengine project follows the rule:
When it is possible (based on gce naming restrictions) we match images by family (system name in the spread.yaml and the family of the desired image), this guarantees that when an image is updated (the date in the name changes) so we automatically will use the last image published for that family.
When there is not match by family, the match is done by description.
Base images are images with no any dependency or extra configuration, just the settings needed to boot on gce. Those images are used as baed to create final images with test dependencies which are used for snapd test suite.
Some base images are created as part of the computeengine projects and others are used from other projects like ubuntu-os-cloud.
The criteria for naming base images on computeengine project follows the rule:
To create a base image there are a set of tasks described on the following section "Add new image", to create a final image also there are a set of tasks described on the following section "Update image".
The generated image has not the snapd test dependencies.
The generated image has not the snapd test dependencies.
The generated image has not the snapd test dependencies.
The generated image nither has the snapd test dependencies installed nor the configuration needed to run snapd tests.
Fedora 26 has just one image, there is not base image asociated, and it has not test dependencies installed and SElinux is not configured as permissive.
The generated image has not SElinux configured as permissive, so snapd tests will fail. The image has the snapd test dependencies installed.
The generated image has not SElinux configured as permissive, so snapd tests will fail. The image has the snapd test dependencies installed.
The update tasks are intended to update a base image installing test dependencies and updating and configuring the system to run the snapd test suite optimally. Some update tasks use base images which are generated on the computeengine project, and other take images from other projects such as we get ubuntu-1604-lts images from project ubuntu-os-cloud.
This task is not working yet, it will done when amazon linux it is supported by snapd.
The generated image has the snapd test dependencies installed and configuration needed to run the snapd test suite.
The generated image has the snapd test dependencies installed.
The generated image has the snapd test dependencies installed.
The generated image has the snapd test dependencies installed and configuration needed to run the snapd test suite.
The generated image has the snapd test dependencies installed and configuration needed to run the snapd test suite.
The base image used for this is the one provided by opensuse-cloud/opensuse-leap-42-3-v20180116
The generated image has the snapd test dependencies installed and configuration needed to run the snapd test suite.
The base image used for this is the one provided by ubuntu-os-cloud/ubuntu-1404-lts
The generated image has the snapd test dependencies installed and configuration needed to run the snapd test suite.
The generated image has the snapd test dependencies installed and configuration needed to run the snapd test suite.
The base image used for this is the one provided by ubuntu-os-cloud/ubuntu-1604-lts
The generated image has the snapd test dependencies installed and configuration needed to run the snapd test suite.
The base image used for this is the one provided by ubuntu-os-cloud/ubuntu-1804-lts
The generated image has SElinux configured as permissive and the test dependencies installed.
The bash script run_google_task.sh is used to run tasks for google backend and then restore the images based on the snapd tests results.
To run a specific task it is just needed to pass the task name, such as:
In case the task finishes successfully, then the snapd tests are executed (just when the image is available on snapd spread.yaml). If snapd tests finish with errors, then the new image is deleted. In case the image which is gonna be deleted is the only one for its family, it is not deleted.
The script is downloading spread and snapd inside the current directory to run the validation, so the avoid uploading them as part of the spread project the script has to be executed from a different path.
Maintenance tasks for images used by spread.
Spread Images | Free Vectors, Stock Photos & PSD
GitHub - snapcore/ spread - images : Maintenance tasks for images used by...
(PDF) Spread spectrum image steganography
200+ Free Spreading & Spread Illustrations - Pixabay
Point spread function - Wikipedia
In this paper, we present a new method of digital steganography, entitled spread spectrum image steganography (SSIS). Steganography, which means "covered writing" in Greek, is the science of communicating in a hidden manner. Following a discussion of steganographic communication theory and review of existing techniques, the new method, SSIS, is introduced. This system hides and recovers a message of substantial length within digital imagery while maintaining the original image size and dynamic range. The hidden message can be recovered using appropriate keys without any knowledge of the original image. Image restoration, error-control coding, and techniques similar to spread spectrum are described, and the performance of the system is illustrated. A message embedded by this method can be in the form of text, imagery, or any other digital signal. Applications for such a data-hiding scheme include in-band captioning, covert communication, image tamperproofing, authentication, embedded control, and revision tracking.
Content may be subject to copyright.
Content uploaded by Charles G. Boncelet
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by Frederick S. Brundick and Lisa M. Marvel
Approved for public release; distribution is unlimited.
... Privacy protection of communication between two parties has been a hot topic on the internet for a long time, and the privacy protection of communication involved [1] can be combined with information hiding, thereby achieving secure communication [2]. Image steganography [3] is an important part of information hiding. The sender hides the message in the carrier image to obtain a secret image (stego), and sends this to the receiver. ...
... Xin et al. [7] embed information based on the complexity of the R, G, and B three-channel textures, which is different from the strategy of equally distributing secret information among the R, G, and B three channels. The maximum load rate of the carrier image is 0. 3 Figure 1. The sample example comes from SteganoCNN, an information hiding system that hides two secret images. ...
Image-to-image steganography is hiding one image in another image. However, hiding two secret images into one carrier image is a challenge today. The application of image steganography based on deep learning in real-life is relatively rare. In this paper, a new Steganography Convolution Neural Network (SteganoCNN) model is proposed, which solves the problem of two images embedded in a carrier image and can effectively reconstruct two secret images. SteganoCNN has two modules, an encoding network, and a decoding network, whereas the decoding network includes two extraction networks. First, the entire network is trained end-to-end, the encoding network automatically embeds the secret image into the carrier image, and the decoding network is used to reconstruct two different secret images. The experimental results show that the proposed steganography scheme has a maximum image payload capacity of 47.92 bits per pixel, and at the same time, it can effectively avoid the detection of steganalysis tools while keeping the stego-image undistorted. Meanwhile, StegaoCNN has good generalization capabilities and can realize the steganography of different data types, such as remote sensing images and aerial images.
... During the last decade, many steganographic algorithms for digital images have been proposed [1] [2] [3]. The image steganography algorithmcan be classified in two classes by its embedding domain: spatial domain embedding method and frequency domain embedding method. ...
In this paper, a method based on multiple features formed by statistical moments of wavelet subband coefficients is proposed. Detection of stego-image created by the steganography based on bit plane complex segmentation (BPCS) has showed a low detection rate. So the evaluation of this method is realized in terms classification error rates using 100 natural images and 100 stego images which are generated by using BPCS steganography. This work proposes to use a support vector machine (SVM) as classifier for BPCS steganography. We have chosen the horizontal and diagonal details of wavelet subband coefficients, since it contains the high frequency components. This is because the data might be hidden in the noisy regions (since stego image should not have any visual artifact) which have high frequency components.
... Early steganography methods (Marvel et al., 1999; Gopalan, 2003) use image and audio as the cover signal because they have a high information theoretic entropy. However, sending an image or audio recording abruptly though a public channel will likely cause the eavesdropper's suspicion. ...
... The steganography methods are classified based on data embedding algorithms. The methods from these classes are discussed in [4][5][6][7][8][9][10][11][12] [13] . The stago attacks and requirements of a steganographic system have been comprehensively discussed in [14][15][16]. ...
Telemedicine is the use of Information and Communication Technology (ICT) for clinical health care from a distance. The exchange of radiographic images and electronic patient health information/records (ePHI/R) for diagnostic purposes has the risk of confidentiality, ownership identity, and authenticity. In this paper, a data hiding technique for ePHI/R is proposed. The color information in the cover image is used for key generation, and stego-images are produced with ideal case. As a result, the whole stego-system is perfectly secure. This method includes the features of watermarking and steganography techniques. The method is applied to radiographic images. For the radiographic images, this method resembles watermarking, which is an ePHI/R data system. Experiments show promising results for the application of this method to radiographic images in ePHI/R for both transmission and storage purpose.
The confidentiality and safety of patient records is a significant concern for medical professionals. So protections must be placed to guarantee that illegal individuals do not have access to medical images (Patient’s description). Hence, the objective of this study is to secure digital medical images being transmitted over the internet from being accessed by an intruder. The study, therefore, proposed a modified Least Significant Bit (LSB) algorithm implemented on a MATLAB 2018a programming environment, and the proposed system was compared with the existing system using three performance metrics which are PSNR. MSE and SSIM. The result showed that the proposed approach outperformed the current standard methods by producing a more robust, high capacity, and highly imperceptible stego image. The comparative analysis conducted also showed that the PSNR valve is higher, and MSE value is lower when compared with existing systems. It was concluded that the projected technique accomplishes excellently in making the medical image transmitted to be more secured, robust, and invisible, thereby making the communication to be unnoticeable by an intruder or attacker.
The recent developments of enormous computer networks have invoked insecurities related to copyright theft of digital media. To be precise, the virtual sharing of medical images over networks with a novel desire of improved medical diagnosis has led to the tampering of sensitive patient identity information. In this chapter, the authors have exemplified the need of watermarking with fragile medical image watermarking using saliency and phase congruency. Initially, the saliency and phase congruency methodologies are applied on the original medical image to highlight the object features. Based on the feature map, a mask is generated which segregates the area of interest from the portions containing visual medical information. An encrypted text, containing identity of the patient, is embedded into the area of interest of the image. The results of imperceptibility and fragility criteria are satisfactory towards the implementation of a fragile watermark as the extracted watermark is found to be corrupted upon unfaithful image processing modifications.
G.711 is the most popular speech codec for Voice over IP (VoIP). This chapter proposes a method for embedding data into G.711-coded speech for conveying side information for enhancing speech quality such as bandwidth extension or packet loss concealment. The proposed method refers to a low-bit rate encoder to determine how many bits are embedded into each sample. First, a variable-bit rate data hiding method is proposed as a basic framework of the proposed method. Then, the proposed method is extended to achieve fixed bit rate data hiding. According to comparison experiments, the proposed method is proved to achieve higher speech quality compared with the conventional method. Moreover, the authors developed a low-complexity speech bandwidth extension method that uses the proposed data hiding method.
In this paper, the authors examine embedding efficiency, which influences the most concerned performance of steganography, security, directly. Embedding efficiency is defined as the number of random message bits embedded per embedding change. Recently, matrix embedding has gained extensive attention because of its outstanding performance in boosting steganographic schemes’ embedding efficiency. Firstly, the authors evaluate embedding change not only on the number of changed coefficients but also on the varying magnitude of changed coefficients. Secondly, embedding efficiency of matrix embedding with different radixes is formularized and investigated. A conclusion is drawn that ternary matrix embedding can achieve the highest embedding efficiency.
Digital image authentication refers to all the techniques performing anti-falsification, digital image copyright protection, or access control. A large number of DIA techniques have been developed to authenticate digital images, including cryptography-based digital image authentication (CBDIA) techniques and data-hiding-based digital image authentication (DHBDIA) techniques. This paper not only provides some practical applications on image authentication, but also describes general frameworks of image watermarking and the general techniques, including robust watermarking, fragile watermarking, and semi-fragile watermarking. This paper also addresses the potential issues on future research directions, including managing the PRNU database, development of advanced PRNU-based blind authentication techniques, and search for digital fingerprints.
Discover more about: Steganography
We present a new method of embedding information within digital
images, called spread spectrum image steganography (SSIS).
Steganography, which means “covered writing” in Greek, is
the science of communicating in a hidden manner. SSIS conceals a message
of substantial length within digital imagery while maintaining the
original image size and dynamic range. The hidden message can be
recovered ... [Show full abstract] using the appropriate keys without any knowledge of the
original image. Image processing, error control coding, and spread
spectrum techniques used to conceal the hidden data are described, and
the performance of the technique is illustrated. The message embedded by
this method can be in the form of text, imagery, or any other digital
signal. Applications for such a data-hiding scheme include in-band
captioning, hidden communication, image tamperproofing, authentication,
invisible map overlays, embedded control, and revision tracking
June 1998 · Lecture Notes in Computer Science
. In this paper we present a new method for reliable blind image steganography that can hide and recover a message of substantial length within digital imagery while maintaining the original image size and dynamic range. Image processing, error-control coding, and spread spectrum techniques are utilized to conceal hidden data and the performance of the technique is illustrated. The message ... [Show full abstract] embedded by this method can be in the form of text, imagery, or any other digital signal. Applications for such a data-hiding scheme include in-band captioning, hidden communication, image tamperproofing, authentication, embedded control, and revision tracking. 1 INTRODUCTION Digital steganography, or information hiding, schemes can be characterized by utilizing the theories of communication [1]. The parameters of information hiding such as the amount of data bits that can be hidden, the perceptibility of the message, and its robustness to removal can be related to the characteristics of communicati...
We present a method of embedding information within digital
images, called spread spectrum image steganography (SSIS) along with its
payload capacity. Steganography is the science of communicating in a
hidden manner. SSIS conceals a message of substantial length within
digital imagery while maintaining the original image size and dynamic
range. The hidden message can be recovered using the ... [Show full abstract] appropriate keys
without any knowledge of the original image. The capacity of the
steganographic channel is described and the performance of the technique
is illustrated. Applications for such a data hiding scheme include
in-band captioning, hidden communication, image tamperproofing,
authentication, invisible map overlays, embedded control, and revision
tracking
A new method of steganalysis for images with embedded messages is presented. We consider two embedding methods: least significant bit (LSB) replacement and plusmn1 LSB embedding. Our method uses lossless image compression to generate statistics that are fed into a support vector machine classifier. We compare results against the pairs method, one of the best existing LSB steganalysis methods. ... [Show full abstract] Both our method and pairs performs well for LSB replacement. However, while pairs cannot detect plusmn1 LSB embedding at all, our method can.
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