With the advancement of 5G networks, the deploy-ment of Internet of Things (IoT) technology has seen significant growth. Blockchain technology, recognized for its strong security features, is increasingly utilized within the IoT domain. However, the current IoT landscape is characterized by challenges such as substantial resource consumption, limited throughput capacity, and insufficient security protocols, which hinder its optimal per-formance. Towards addressing such problems, we propose a con-sensus algorithm called Proof-of-GoS (PoG) based on the grade of service (GoS), in which a node must have a service score over a set score threshold to be allowed to join the consensus process. The correct behavior of a node results in a reward, while any malicious actions result in penalties. Finally, we simulate a network to evalu-ate the performance and security of PoG and compare it with sev-eral existing consensus algorithms. The experimental findings in-dicate that the proposed PoG consensus algorithm retains the fun-damental security properties of blockchain and outperforms the state-of-the-art consensus mechanisms.

Contrast enhancement methods based on reversible data hiding (RDHCE) can be used for contrast enhancement of medical images, which is a hot research topic in recent years. However, the region of interest (ROI) of medical images cannot be accurately segmented using the current RDHCE algorithms and histogram pixels clustering in medical images results in incorrect localization of longer intervals, which affects the contrast enhancement effect of images. In this paper, the Unet3+ network model is used, which makes the segmented ROI region and ROI histogram clearer and more accurate than those obtained by the traditional segmentation methods and the algorithm integrates a larger embedding capacity and a better visual quality of the image. It adaptively determines and stretches the interval of the ROI greyscale histogram and at the same time enlarges the embedding capacity of the ROI to enhance the contrast of the image. The proposed algorithm improves the visual quality of medical images by 20% and enhances ROI embedding capacity by 25% compared to existing methods.

Reversible data hiding-based contrast enhancement can be applied to medical images, which not only allows the storage of patient information through reversible embedding, but also achieves image contrast enhancement, thereby assisting doctors in accurately diagnosing patient diseases. In response to the existing problems of mainstream methods, a novel reversible data hiding-based local contrast enhancement method for medical images is proposed. This method utilizes superpixel segmentation to segment medical images into multiple pixel blocks, and performs reversible data embedding and contrast enhancement for the pixel blocks within the region of interest (ROI). Additionally, a new embedding strategy is proposed. According to the contrast and texture features of each pixel block, histogram expansion of different degrees is carried out to effectively enhance the pixel blocks with low contrast, while avoiding excessive enhancement of the pixel blocks with high contrast. Experimental results demonstrate that, compared with the state-of-the-art mainstream methods, the proposed method not only improves the contrast in the ROI but also ensures high visual quality of the medical images.

With the popularization of digital information, reversible data hiding in ciphertext has become a critical research focus in privacy protection in cloud storage. A reversible data hiding method for encrypted images is proposed: Reversible Data Hiding in Encrypted Images with Adaptive Multi-directional MED and Huffman Code based on Interval-Wise Dynamic Prediction Axes (RDHEI-AHIDA). Firstly, the original image is predicted by the gradient Adaptive Multi-Directional Median Edge Detector (AM-MED) to obtain the critical gradient and the position of the Interval-wise Dynamic Prediction Axes (IDP-Axes). Then, information bits are allocated at intervals on the IDP-Axes. Combining the determined position of the IDP-Axes and the critical gradient, the prediction error values of the original image are calculated and recorded. After the image is encrypted, according to the distribution of prediction error values, an adaptive Huffman code rule is established, and pixel marking, classification and auxiliary information embedding are carried out. Finally, the secret data is embedded by the bit replacement method. Compared with the state-of-the-art RDHEI methods, experimental results show that RDHEI-AHIDA not only provides a higher pure payload while ensuring security but also exhibits certain robustness.

In real-world applications, screen capturing represents a significant scenario where this process can induce substantial distortion to the original image. Previous methods for simulating screen-shooting distortion often involved combining different formulas. We found that these simulation methods still have a significant gap compared to real distortions, making it urgently necessary to develop a realistic and credible comprehensive noise layer to achieve robustness against screen-shooting distortion. This paper presents a watermarking scheme capable of withstanding severe screen-shooting distortion. First, a dataset is constructed to train a screen-shooting distortion simulation network based on style transfer. Subsequently, a comprehensive noise layer is built upon this network to achieve robustness against severe screen-shooting distortion. Additionally, this paper incorporates structural re-parameterization techniques into the traditional U-shaped encoder to improve the quality of encoded images. Extensive experiments demonstrate the proposed scheme’s superior performance in terms of robustness and generalization, especially under severe screen-shooting distortion conditions.

Purely chromatic background images are widely used in computer wallpapers and advertisements, leading to issues such as copyright infringement and the loss of interest of holders. Image hashing is a technique used for comparing the similarity between images, and is often used for image verification, search, and copy detection due to its insensitivity to subtle changes in the original image. In a purely chromatic background image, the central detail of the image is the primary part and the key for copyright authentication. As the perception hash (pHash) algorithm only retains the low-frequency portion of the discrete cosine transform (DCT) matrix, it is unsuitable for purely chromatic background images. To deal with this issue, we propose an improved perception hash (ipHash) algorithm to enhance the universality of the algorithm by extracting purely chromatic background image features. Meanwhile, the development of image hashing is restricted due to the requirement of a trusted third party. To solve this issue, a secure blockchain-based image copyright protection scheme is designed. It realizes the copyright authentication and traceability, and overcomes the issue of a lack of trusted third parties. Experimental results show that the proposed method outperforms the state-of-theart image copyright protection schemes.

The robustness of existing robust reversible watermarking (RRW) against attacks cannot satisfy requirement for image copyright protection of industrial Internet of Things. To address these issues, in this article, we propose an efficient robust reversible watermarking scheme based on ZMs and integer wavelet transform for industrial images. First, Zernike moments (ZMs) in the low-frequency region of integer wavelet transform coefficients of the original image are calculated. Then, the multibit watermarking is reversibly embedded into the magnitudes of the ZMs. The errors generated when embedding the watermark are reversibly embedded into the pixels of the image. Later, the receiver can utilize the errors extracted from the watermarked image to restore the original image. In the case of images being attacked, the extracted robust watermark can be used as a proof of copyright authentication.The experimental results demonstrate that the proposed method can not only completely restore the original industrial image when the watermarked image is not attacked, but also has stronger robustness against geometric transformations, common image processing operations and joint attacks compared with the state-of-the-art RRW authentication schemes.

Reversible data hiding-based contrast enhancement (RDHCE) can be used in contrast enhancement for medical images, and it has been a popular research topic in recent years. However, the existing RDHCE methods suffer from the problem of inaccurate segmentation of the region of interest (ROI) in medical images, which can impact the contrast enhancement effect of the images. Moreover, some methods face limitations in their universality for ROI histograms with few empty bins on both sides, which results in unsatisfactory embedding capacity and contrast enhancement effect. To solve these problems, this study proposes an improved RDHCE method for medical images. The proposed method uses the UNet3+ network model, which makes the segmented ROI histograms more consistent with the subjective judgment of doctors compared to those obtained by traditional segmentation approaches. In addition, a multi-group stretching method is proposed to address the limitation of histogram expansion caused by the empty bins on both histogram sides, enabling adaptation to different ROI histograms with varying gray distributions. Compared to state-of-the-art RDHCE methods, the proposed method offers better generalizability, superior contrast enhancement performance and a larger ROI embedding capacity. It can greatly improve the visual quality of medical images in the field of medical imaging and aid doctors in making more accurate diagnoses.

With the development of data security and privacy requirements in the field of cloud computing, Reversible Data Hiding in Encrypted Images (RDHEI) in encryption domain has received increasing attention. In order to take full advantage of the spatial and textural features of the original image, reversible data hiding in encrypted image with adaptive Huffman code based on Dynamic Prediction Axes (RDHEI-HDA) is proposed. First, the prediction errors of the original plaintext image are calculated according to the multidirectional median edge detector (M-MED) combined with the Dynamic Prediction Axes which are generated by the spatial correlation of the original image. After encryption process with the stream cipher, the adaptive Huffman coding labeling rule is created for pixel labeling and classification according to the Dynamic Prediction Axes and the distribution of prediction errors. Finally, bit substitution is employed to insert secret data and side information into the image. In Comparison to most of the state-of-the-art RDHEI methods, the experimental results show that the RDHEI-HDA method provides a higher pure payload while ensuring safety.

The Graphical User Interface (GUI) plays a critical role in the interaction between users and mobile applications (apps), aiming at facilitating the operation process. However, due to the variety of functions and non-standardized design, GUIs might have many accessibility issues, like the size of components being too small or their intervals being narrow. These issues would hinder the operation of low vision users, preventing them from obtaining information accurately and conveniently. Although several technologies and methods have been proposed to address these issues, they are typically confined to issue identification, leaving the resolution in the hands of developers. Moreover, it can be challenging to ensure that the color, size, and interval of the fixed GUIs are appropriately compared to the original ones. In this work, we propose a novel approach named AccessFixer (Accessibility IssuesFixing Method), which utilizes the Relational-Graph Convolutional Neural Network (R-GCN) to simultaneously fix three kinds of accessibility issues, including small sizes, narrow intervals, and low color contrast in GUIs. With AccessFixer, the fixed GUIs would have a consistent color palette, uniform intervals, and adequate size changes achieved through coordinated adjustments to the attributes of related components. Our experiments demonstrate the effectiveness and usefulness of AccessFixer in fixing GUI accessibility issues. After fixing 30 real-world apps, our approach solves an average of 81.2% of their accessibility issues. Compared with the baseline tool that can only fix size-related issues, AccessFixer not only fixes both the interval and color contrast of components, but also ensures that no new issues arise in the fixed results. Also, we apply AccessFixer to 10 open-source apps by submitting the fixed results with pull requests (PRs) on GitHub. The results demonstrate that developers approve of our submitted fixed GUIs, with 8 PRs being merged or under fixing. A user study examines that low vision users host a positive attitude toward the GUIs fixed by our method.

Aiming at data security in wireless sensor networks (WSNs), a data authentication scheme based on a double watermark is proposed in this article. Double watermark includes reversible watermark and irreversible watermark. The former generated by group head data is embedded into the effective precision bit of data, and the latter generated by the effective precision bit of the data themselves and a defined array is embedded into the flag bit in two cases. Two adjacent groups form a working group. For the first case, a flag-check matrix is constructed by the special data set which includes the head of the current group and the tail of the previous group, and the data around them. Through the matrix, the watermark is embedded into the special data set to ensure the robustness of grouping. For the second case, watermark is embedded into the group data except special data set (the rest data) to ensure efficient detection of attacks. When the attacked position and type are detected, the data that are not tampered with in this group can still recover the effective precision bit. The security analysis and experimental results demonstrate that the comprehensive performance of the proposed scheme outperforms that of those state-of-the-art schemes.

Image encryption is used for privacy protection in cloud computing. Nowadays, reversible data hiding in encrypted image (RDHEI) has achieved great success with the demand of embedding additional information into encrypted image. The existing algorithms cannot implement large embedding capacity and good reconstructed image quality simultaneously. Besides, the universality of some methods is limited when they are used in images with different textural characteristics. In this work, an RDHEI method based on most significant bit (MSB) prediction and error embedding is proposed. On one hand, all types of prediction errors are considered in the proposed method, therefore all the pixels with prediction errors can be recovered correctly. On the other hand, error blocks are utilized to mark the locations of prediction errors and message blocks are utilized to embed data. Moreover, flag blocks are utilized to distinguish error and message blocks. To solve the problem of misjudgement for flag blocks in the decoding phase, special operations are conducted on error blocks and message blocks. Experimental results demonstrate that, compared with the state-of-the-art RDHEI methods, the proposed method has good universality on well-known databases.

Data authentication is an important part of wireless sensor networks (WSNs). Aiming at the problems of high false positive rate and poor robustness in group verification of existing reversible watermarking schemes in WSNs, this paper proposes a scheme using reversible watermarking technology to achieve data integrity authentication with high detection efficiency (DAHDE). The core of DAHDE is dynamic grouping and double verification algorithm. Under the condition of satisfying the requirement of the group length, the synchronization point is used for dynamic grouping, and the double verification ensures that the grouping will not be confused. According to the closely related characteristics of adjacent data in WSNs, a new data item prediction method is designed based on the prediction‐error expansion formula, and a flag check bit is added to the data with embedded watermarking during data transmission to ensure the stability of grouping, by which the fake synchronization point can be accurately identified. Moreover, the embedded data can be recovered accurately through the reversible algorithm of digital watermarking. Analysis and experimental results show that compared with the previously known schemes, the proposed scheme can avoid false positive rate, reduce computation cost, and own stronger grouping robustness.

Conventional authentication schemes that use fragile watermarks can often lead to irreversible image distortion. Therefore, reversible authentication (RA) schemes, which do not lead to the introduction of distortion, have become increasingly popular. However, existing RA schemes need the help of third-party services for tamper localization or content recovery, and hence are not completely blind, which may result in inconvenience and insecurity of the authentication procedure. Motivated by this issue, a new RA scheme is proposed using the prediction-error expansion and compressed sensing reconstruction techniques. The proposed scheme achieves basic authentication in a reversible way, and implements tamper localization and content recovery without any third-party dependency. Experimental results demonstrate the proposed scheme has better performance compared to other state-of-the-art RA schemes.

The conventional reversible data hiding (RDH) algorithms pursue high Peak-Signal-to-Noise-Ratio (PSNR) at the certain amount of embedding bits. Recently, Wu et al. deemed that the improvement of image visual quality is more important than keeping high PSNR. Based on this viewpoint, they presented a novel RDH scheme, utilizing contrast enhancement to replace the PSNR. However, when a large number of bits are embedded, image contrast is over-enhanced, which introduces obvious distortion for human visual perception. Motivated by this issue, a new RDH scheme is proposed using the controlled contrast enhancement (CCE) and Haar integer wavelet transform (IWT). The proposed scheme has large embedding capacity while maintaining satisfactory visual perception. Experimental results have demonstrated the effectiveness of the proposed scheme.