Advanced Information Securing by Combining Fortified Binary Image Steganography and Asymmetric Encryption Standard

The aim of this study is to hide two binary BMP images in a single BMP grayscale image. The widespread technique in image steganography is to hide one image (stego image) into another (cover image). The proposed novel method is to hide two binary images into one grayscale bitmap cover image. First of all, the proposed technique starts with transforming all grayscale cover image pixels into multiples of five using Five Modulus Method (FMM). Clearly, any modulus of five is either 0, 1, 2, 3, or 4. The transformed FMM cover image could be treated as a good host for carrying data. Obviously, it is known that the pixel value for the binary image is either 0 or 1. Therefore, by concatenating the two binary images, the composite results are 00, 01, 10 and 11. In fact, these concatenated values could be mapped using simple mapping that assigns a positive integer value such as 1 for 00, 2 for 01, 3 for 10 and 4 for 11. Consequently, a new matrix will be constructed that contains a number varying from 1 to 4 only. Fortunately, the four integer values are the same as the previously mentioned reminders of division by 5, hence, adding these four integers to the transformed FMM cover image. On the recipient side, a reverse process will be implemented to extract the two binary images. In terms of PSNR values, the cover image and the two extracted stego images have acceptable PSNR values, which yields that the proposed method is very efficient in information hiding.

This paper presents the transform domain image steganography schemes using three popular matrix factorization techniques and contourlet transform. It is known that security of image steganography is mainly evaluated using undetectability of stego image when steganalyzer examines it in order to detect the presence of hidden secret information. Good imperceptibility only suggests eavesdropper’s inability to suspect about the hidden information; however stego image may be analyzed by applying certain statistical checks when it is being transmit- ted through the channel. This work focusses on improving undetectability by employing ma- trix decomposition techniques along with transform domain image steganography. Singular value decomposition (SVD), QR factorization, Nonnegative matrix factorization (NMF) are employed to decompose contourlet coefficients of cover image and secret is embedded into its matrix factorized coefficients. The variety of investigations include the effect of matrix decomposition techniques on major attributes of image steganography like imperceptibility, robustness to a variety of image processing operations, and universal steganalysis perfor- mance. Better imperceptibility, large capacity, and poor detection accuracy compared to existing work validate the efficacy of the proposed image steganography algorithm. Compa- rative analysis amongst three matrix factorization methods is also presented and analyzed.

The mass dispersal of digital communication requires the special measures of safety. The need for safe communication is greater than ever before, with computer networks now managing almost all of our business and personal affairs. Information security has become a major concern in our digital lives. The creation of new transmission technologies forces a specific protection mechanisms strategy particularly in data communication state.
 We proposed a steganography method in this paper, which reads the message, converting it into its Residue Number System equivalent using the Chinese Remainder Theorem (CRT), encrypting it using the Rivest Shamir Adleman (RSA) algorithm before embedding it in a digital image using the Least Significant Bit algorithm of steganography and then transmitting it through to the appropriate destination and from which the information required to reconstruct the original message is extracted. These techniques will enhance the ability to hide data and the hiding of ciphers in steganographic image and the implementation of CRT will make the device more efficient and stronger. It reduces complexity problems and improved execution speed and reduced the time taken for processing the encryption and embedding competencies.

Adaptive image steganalysis algorithm is one of the important research direction in the field of information security. Nowadays, digital image steganography based on deep learning has been widely used to protect sensitive data during transmission or storage. However, it is a great challenge to achieve the accurate location of the best steganography position in the cover image and improve the model training accuracy is a great challenge. To solve the above problems, a novel digital image steganography algorithm based on reinforcement learning—VERY-RL, is proposed to obtain the optimal embedding strategy. Meanwhile, according to the designed distortion function, the parameters of VERY-RL are updated by gradient backpropagation to minimize the embedding loss. Experimental results show that, compared with other steganographic methods based on reinforcement learning, the detection error rate of VERY-RL is improved by 21.94% when resisting steganalysis detection. In addition, the FLOPs of the model are reduced by 24.8%. It can be seen that VERY-RL improves the localization accuracy of pixel modification bits, and the steganography network has a stronger representation ability, indicating that VERY-RL has better steganographic security.

Digital medical images are becoming even more widespread and used in a large variety of applications. Such images are often stored in local repositories and need to be transmitted/ received over the network. Therefore, data compression is an essential mechanism to adopt for improving the transmission time, as well as to optimize the required storage space. However, due to the clinical relevance of such data, lossless techniques are almost ever preferred, given that in such context it is not tolerated any loss of data. In this work, starting from the Medical Images Lossless Compression (MILC) algorithm, which enables the efficient coding of three-dimensional medical images achieving results comparable with the other state-of-art algorithms, we propose Parallel MILC, a fully parallelized version of that algorithm, which provides some attractive features with respect to MILC, especially in terms of execution speedup and scalability. In detail, we propose a novel design and implementation for the MILC algorithm, which is able to exploit the power and the capabilities of the parallel computing paradigm. It is important to point out that Parallel MILC can be executed on several heterogeneous device types supporting OpenCL (i.e. CPU, GPU, FPGA, etc.). The preliminary test results show a significant performance speedup of Parallel MILC compared to MILC. Consequently, the novel algorithm we propose provides MILC with strong scalability properties and complete transparency with respect the underlying hardware.

With advancements in X-ray technology, there is an increase in the number of digital images used in the diagnosis of a patient. Whether it be a simple X-ray, MRI, CT scan or even a photo taken from a camera, the rise in the use of digital images has increased sharply. Though this has eased the entire process, it has brought the threat of cyber-attacks and breaches. The proposed method bridges this existing gap by incorporating suitable security mechanisms to preserve the privacy and confidentiality of medical diagnostic information of an individual. The approach utilizes neural networks to perform image steganography and combines it with a cryptographic algorithm (RSA) to secure medical images. The proposed method uses a two-level security providing a lower loss of 0.002188 on medical images improving upon existing image steganography techniques.

Still (or intra-picture) image coding includes compression of binary images and images with multiple amplitude levels, the latter case either monochrome or with multiple color components, as typically captured by cameras or image scanners. Beyond that, compression schemes are also applied to other sensor data such as magnetic resonance intensity in medical imaging, infrared, x-ray, depth captured by range sensors, or to synthetic content, specifically generated by computer graphics or captured from screens. Various methods such as run-length coding and conditional entropy coding for binary images; vector quantization, predictive coding and transform coding for multi-amplitude images are presented in more detail. Further sections discuss lossless image compression, synthesis based coding, fractal coding and 3D image coding. As examples for the overall combination of building blocks, still image compression standards like JPEG, JPEG 2000 and the intra-picture compression of AVC and HEVC video coding standards are discussed in detail.KeywordsSide InformationHigh Efficiency Video CodeJoint Photographic Expert GroupPredictive CodeIntra PredictionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In today’s organizations, a vast amount of existing software systems is insecure, which results in compromised valuable assets and has negative consequences on the organizations. Throughout the years, many attempts have been made to build secure software systems, but the solutions proposed were limited to a few add-on fixes made after implementation and installation of the system.The contribution of the research in this thesis is a software security engineering methodology, called Controlled Security Engineering Process, which provides support to developers when developing more secure software systems by integrating software lifecycle and security lifecycle, and enhancing the control in the engineering process. The proposed methodology implements security in every phase of general software system engineering, i.e., requirement, design, implementation, and testing, as well as operation and maintenance to certify that software systems are built with security in mind.The Controlled Security Engineering Process methodology addresses security problems in the development lifecycle. Construction of a secure software system involves specific steps and activities, which include security requirements specifications of system behavior, secure software design, an analysis of the design, implementation, with secure coding and integration, and operating and maintenance procedures.The methodology incorporates software security patterns and control of the engineering process. The software security patterns can be used as security controls and information sources to demonstrate how a specific security task should be performed or a specific security problem solved. Many patterns can be implemented in an automated way, which can facilitate the work of software engineers.The control of the engineering process provides visibility over the development process. The control assures that authorised developers access legitimate and necessary information and projects’ documents by using authentication, and authorization.To support implementation of automated patterns and provide control over the engineering process, a design of a multi-agent system is provided. The multi-agent system supports implementation of patterns and extracting security information, and provides traceability in the engineering process. The security information is requirements, threats and security mechanisms that are provided by matching project documents, and traceability is achieved by monitoring and logging services.The Controlled Security Engineering Process methodology has been evaluated through interviews with developers, security professionals, and decision makers in different types of organizations but also through a case study which was carried out in an organization.

BI&T Tell me about the Medical Imaging and Technology Alliance. David Fisher The Medical Imaging & Technology Alliance (MITA), a division of the National Electrical Manufacturers Association (NEMA), is the leading organization and collective voice of medical imaging equipment manufacturers, innovators, and product developers. It represents companies whose sales comprise more than 90 percent of the global market for medical imaging technology. The goals of MITA are to: • Increase the awareness and understanding of the value of medical imaging • Achieve efficient and reasonable regulation of medical imaging technologies • Interact with appropriate government agencies on reimbursement and technology assessment policies • Expand the global acceptance of the digital communications standard (DICOM) that allows digital imaging technologies to interact seamlessly • Improve regulatory harmonization of the global market for medical imaging products • Develop and represent industry positions in technical, trade and other issues • Provide market data unique to this industry

Steganography is the art/technique of hiding message data inside a carrier file in such a way that unauthorized or unsolicited personnel is not capable of detecting the presence of data inside the carrier file. The Proposed Method provides improved security and improved high embedding capacity image steganography through the usage of Integer Wavelet Transform (IWT) and Chaotic Logistic map. Least Significant Bit technique is used to replace the bits in the coefficient of detail band. The proposed method offers lossless and unnoticeable change in the image steganography. In this paper we focus on both cryptography and steganography for better confidentiality, security and robustness. We find that the proposed algorithm has a better CMPSNR (Chaotic Logistic mapping) value averaging close to 74 after embedding the secret data, while the existing algorithms have values of around 65.

Secret information sharing through image carriers has aroused much research attention in recent years with images’ growing domination on the Internet and mobile applications. The technique of embedding secret information in images without being detected is called image steganography. With the booming trend of convolutional neural networks (CNN), neural-network-automated tasks have been embedded more deeply in our daily lives. However, a series of wrong labeling or bad captioning on the embedded images has left a trace of skepticism and finally leads to a self-confession like exposure. To improve the security of image steganography and minimize task result distortion, models must maintain the feature maps generated by task-specific networks being irrelative to any hidden information embedded in the carrier. This paper introduces a binary attention mechanism into image steganography to help alleviate the security issue, and, in the meantime, increase embedding payload capacity. The experimental results show that our method has the advantage of high payload capacity with little feature map distortion and still resist detection by state-of-the-art image steganalysis algorithms.

Retinal colour images play an important role in supporting medical diagnosis. Digital retinal image usually are represented in such a large data volume that takes a considerable amount of time to be accessed and displayed. Digital medical image coding therefore become crucial in medical image transfer and storage in electronic medical record server. This paper is concerned to compare the vector quantization (VQ) coding using K-Means and Fuzzy C-Means algorithms. This research investigates the performance of each algorithm: objective (PSNR value) and subjective (visual). The VQ coding scheme is conducted separately to image components in each RGB channel. Reconstructed colour image is obtained by combining the VQ decoding result of each image channel. The 444 combination (coding of the R, G and B channels by the size of 4×4) produces the best subjective and objective quality of image coding. However, the optimum colour models for teleophthalmology and electronic medical record is 848 combination due to the file size, objective and subjective quality.

A new image steganography method with optimum pixel similarity for data hiding in medical images

Steganography is the art of hiding secret data in a medium whereas visual-cryptography is a cryptographic technique where a full image is encrypted. The security of image steganography depends on the secret key set which is used for embedding the secret image. If the secret key is exposed, the secret image can be retrieved. To solve this security issue we propose a new type of steganography method using visual cryptography. In this method, text and image are used as a secret message and image for the cover object. 24-bit RGB color images are used as both secret and cover images. In this method, we use a new image namely share1 which converts a secret image to a totally different image called share2 image. Typically, knowing the extraction method, people can retrieve the secret message easily. A pseudorandomly generated image is used as a key for visual encryption and this ensures the extra layer of security. By this proposed method, we ensure more secured image steganography.

The blocking effect is a common problem that is always present in image coding when using the blocking transform. A simple method for image coding, with low blocking effect, using pre- and post-axis rotation and the discrete sine transform, is proposed. It uses the overlap of only the sample and the total number of transform coefficients remains the same as the number of pixels in the original image. The method was implemented, and the results obtained by simulation in the image coding are compared with image coding by DCT, DST, and LOT.