Robust RDH Technique using Sorting and IPVO-based Pairwise PEE for Secure Communication

Nowadays, Reversible Data Hiding (RDH) is used extensively in information sensitive communication domains to protect the integrity of hidden data and the cover medium. However, most of the recently proposed RDH methods lack robustness. Robust RDH methods are required to protect the hidden data from security attacks at the time of communication between the sender and receiver. In this paper, we propose a Robust RDH scheme using IPVO based pairwise embedding. The proposed scheme is designed to prevent unintentional modifications caused to the secret data by JPEG compression. The cover image is decomposed into two planes namely HSB plane and LSB plane. As JPEG compression most likely modifies the LSBs of the cover image during compression, it is best not to hide the secret data into LSB planes. So, the proposed method utilizes a pairwise embedding to embed secret data into HSB plane of the cover image. High fidelity improved pixel value ordering (IPVO) based pairwise embedding ensures that the embedding performance of the proposed method is improved.

Design secure multi-level communication system based on duffing chaotic map and steganography

Cryptography and steganography are among the most important sciences that have been properly used to keep confidential data from potential spies and hackers. They can be used separately or together. Encryption involves the basic principle of instantaneous conversion of valuable information into a specific form that unauthorized persons will not understand to decrypt it. While steganography is the science of embedding confidential data inside a cover, in a way that cannot be recognized or seen by the human eye. This paper presents a high-resolution chaotic approach applied to images that hide information. A more secure and reliable system is designed to properly include confidential data transmitted through transmission channels. This is done by working the use of encryption and steganography together. This work proposed a new method that achieves a very high level of hidden information based on non-uniform systems by generating a random index vector (RIV) for hidden data within least significant bit (LSB) image pixels. This method prevents the reduction of image quality. The simulation results also show that the peak signal to noise ratio (PSNR) is up to 74.87 dB and the mean square error (MSE) values is up to 0.0828, which sufficiently indicates the effectiveness of the proposed algorithm.

A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms

Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in rehabilitation research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires significant neuroanatomical expertise. We previously released a large, open-source dataset of stroke T1w MRIs and manually segmented lesion masks (ATLAS v1.2, N=304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N=955), a larger dataset of T1w stroke MRIs and manually segmented lesion masks that includes both training (public) and test (hidden) data. Algorithm development using this larger sample should lead to more robust solutions, and the hidden test data allows for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke rehabilitation research.

Improving the efficiency of the steganographic method of data hiding with the application of iterative functions and noise addition

The development of computer and digital technology contributes to the growth of information flows transmitted through open and closed communication channels. In many cases, this information is confidential, financial, or commercial in nature and is of value to its owners. This requires the development of mechanisms to protect information from unauthorized access. There are two fundamental areas of secure data transmission over the open communication channels – cryptography and steganography. The fundamental difference between them is that cryptography hides from others the content of the message, and steganography hides the very fact of the message transmission. This paper is devoted to steganographic methods of data concealment, which are less researched than cryptographic, but have significant potential for use in a variety of applications. One of the important characteristics of most methods is their effectiveness. In general, efficiency is assessed in the context of solving specific problems. However, the most common criteria for the effectiveness of steganographic methods are the amount of hidden data and the method of transmitting the secret key to the receiving party, which will not allow the attacker to intercept it. Because media files make up a significant portion of network traffic, a digital image is chosen as the stegocontainer. It is proposed to determine the coordinates of the embedding location on the basis of iterative functions. The advantage of their use is the compactness of the description of the coordinates of the pixels in which the data will be hidden. In addition, it is proposed to use the Diffie-Gellman algorithm to transfer the parameters of iterative functions to the receiving side. This method of key distribution makes the steganographic method less vulnerable to being stolen by an attacker. The second performance criterion is the amount of hidden data. The paper found that the moderate addition of multiplicative noise makes it possible to increase the amount of hidden data without significantly reducing the visual quality of the stegocontainer. To analyze the distortions in the image-stegocontainer, which are due to the influence of noise and modification of the lower bits of pixels, the method of a quantitative assessment of visual quality is used, which is based on the laws of visual perception. Keywords: steganographic data hiding; hiding efficiency; iterative functions; Diffie-Gelman algorithm.

Two-Bit Embedding Histogram-Prediction-Error Based Reversible Data Hiding for Medical Images with Smooth Area

During medical treatment, personal privacy is involved and must be protected. Healthcare institutions have to keep medical images or health information secret unless they have permission from the data owner to disclose them. Reversible data hiding (RDH) is a technique that embeds metadata into an image and can be recovered without any distortion after the hidden data have been extracted. This work aims to develop a fully reversible two-bit embedding RDH algorithm with a large hiding capacity for medical images. Medical images can be partitioned into regions of interest (ROI) and regions of noninterest (RONI). ROI is informative with semantic meanings essential for clinical applications and diagnosis and cannot tolerate subtle changes. Therefore, we utilize histogram shifting and prediction error to embed metadata into RONI. In addition, our embedding algorithm minimizes the side effect to ROI as much as possible. To verify the effectiveness of the proposed approach, we benchmarked three types of medical images in DICOM format, namely X-ray photography (X-ray), computer tomography (CT), and magnetic resonance imaging (MRI). Experimental results show that most of the hidden data have been embedded in RONI, and the performance achieves high capacity and leaves less visible distortion to ROIs.

A Novel and Robust Hybrid Blockchain and Steganography Scheme

Data security and data hiding have been studied throughout history. Studies show that steganography and encryption methods are used together to hide data and avoid detection. Large amounts of data hidden in the cover multimedia distort the image, which can be detected in visual and histogram analysis. The proposed method will solve two major drawbacks of the current methods: the limitation imposed on the size of the data to be hidden in the cover multimedia and low resistance to steganalysis after stego-operation. In the proposed method, plaintext data are divided into fixed-sized bits whose corresponding matching bits’ indices in the cover multimedia are accumulated. Thus, the hidden data are composed of the indices in the cover multimedia, causing no change in it, thus enabling considerable amounts of plaintext to be hidden. The proposed method also has high resistance to known steganalysis methods because it does not cause any distortion to the cover multimedia. The test results show that the performance of the proposed method outperforms similar conventional stenographic techniques. The proposed Ozyavas–Takaoglu–Ajlouni (OTA) method relieves the limitation on the size of the hidden data, and hidden data is undetectable by steganalysis because it is no longer embedded in the cover multimedia.

Implementation Of Encryption And Hidden Data Using Tiny Encryption Algorithm And End Of File

Security and confidentiality of data is one important aspect of an information system. The information can be misused very large losses in high-profile cases such as vital information confidential corporate, customer data banks and etc. Information security solutions in one of them can be used with cryptography. Cryptographic algorithms used in this study is a tiny encryption algorithm. Cryptographic data security attacks can always wear can occur, with this in mind the authors added security techniques to perform data hiding with the media as a placeholder, this term is called steganography. Steganography is used in this study is the end of the file. These techniques make the process of data hiding which is located at the end of the image, so it does not affect the image quality of the reservoir. In this study, a system built using microsoft visual studio 2010 C#. This system can work well, but has a color image blur caused to the container caused by the inserted message, where the greater the size of the message was inserted then color the image blur that arises will be many more.

Network visualisation of synthetic biology designs

Visualising the complex information captured by synthetic biology designs is still a major challenge. The popular glyph approach where each genetic part is displayed on a linear sequence allows researchers to generate diagrams and visualise abstract designs, but only represents a single, static representation that results in visualisation that is not specific to the requirements of a user resulting in a one-size-fits-all visualisation. We developed a network visualisation technique that automatically turns all design information into a graph, displaying otherwise hidden data. The structure of the resulting graphs can be dynamically adjusted according to specific visualisation requirements, such as highlighting proteins, interactions or hierarchy. Since biological systems have an inherent affinity with network visualisation, we advocate for adopting this approach to standardise and automate the representation of complex information.

Property Analysis and Criteria for Detecting Hidden Data in Microsoft Word Documents

In this article, we propose detecting hidden elements in docx documents. Using the feature of the format structure, we analyze the most profitable stegocontainer. It is proposed to consider three options for investing in a stegocontainer in order to obtain the most objective estimates of the deviation. A comprehensive assessment is carried out from the tested data on the basis of which it was proposed to detect hidden elements in Microsoft Word documents.