Histogram Shifting Research Articles

Inter-Channel Correlation Modeling and Improved Skewed Histogram Shifting for Reversible Data Hiding in Color Images

Reversible data hiding (RDH) is an advanced data protection technology that allows the embedding of additional information into an original digital medium while maintaining its integrity. Color images are typical carriers for information because of their rich data content, making them suitable for data embedding. Compared to grayscale images, color images with their three color channels (RGB) enhance data embedding capabilities while increasing algorithmic complexity. When implementing RDH in color images, researchers often exploit the inter-channel correlation to enhance embedding efficiency and minimize the impact on image visual quality. This paper proposes a novel RDH method for color images based on inter-channel correlation modeling and improved skewed histogram shifting. Initially, we construct an inter-channel correlation model based on the relationship among the RGB channels. Subsequently, an extended method for calculating the local complexity of pixels is proposed. Then, we adaptively select the pixel prediction context and design three types of extreme predictors. The improved skewed histogram shifting method is utilized for data embedding and extraction. Finally, experiments conducted on the USC-SIPI and Kodak datasets validate the superiority of our proposed method in terms of image fidelity.

An Effective Multiple-Bit Data Abstraction and Lossless Retrieval without Shifting

Cryptography and Steganography are two major forms of enforcing data privacy, with the former being prevalent. For small payloads, Steganography is preferred, as the data remains concealed and abstracted. The process involves embedding bits onto a carrier image. However, the disadvantage of steganography is that the carrier image gets distorted, which is a major concern when it comes to pixel sensitive images, such as forensic and medical images that aremission critical. Furthermore, due to modifications in the pixel values, the images get distorted, which can be irrecoverable. Hence, the technique of Reversible Data Hiding(RDH) can be used as a solution. Usually two methods are used under RDH, namely Histogram Shifting (HS) and Difference Expansion (DE). Histogram Shifting method shifts all pixel values between pairs of peak and zero points, which allows data to be embedded in the intermediate points. However, this method also depends on frequencies of pixel values on the image, andthe embedding capacity varies greatly on the image. In Difference Expansion Method, the difference between a pixel and its adjacent pixels is considered to embed a single bit intoone of the pixels. Here, a scheme to embed multiple bits in a pixel is proposed, which greatly enhances the embedding capacity. Initially, three horizontally adjacent pixels are takenand the difference between the rightmost and leftmost pixel iscalculated. This difference is used to classify the centre pixel as embeddable or non-embeddable. If the pixel is embeddable, anew pixel value is calculated, which comprises of the secret bits, offset bits and flag bits, without the use of any difference expansion or histogram shifting process. The secret bits and the original pixel value can be easily recovered from the modified value using the offset and flag bits. This scheme has higher embedding capacity, and lower complexity in terms of computations, when comparedwith other RDH schemes.

Influential Metrics Estimation and Dynamic Frequency Selection Based on Two-Dimensional Mapping for JPEG-Reversible Data Hiding.

JPEG Reversible Data Hiding (RDH) is a method designed to extract hidden data from a marked image and perfectly restore the image to its original JPEG form. However, while existing RDH methods adaptively manage the visual distortion caused by embedded data, they often neglect the concurrent increase in file size. In rectifying this oversight, we have designed a new JPEG RDH scheme that addresses all influential metrics during the embedding phase and a dynamic frequency selection strategy with recoverable frequency order after data embedding. The process initiates with a pre-processing phase of blocks and the subsequent selection of frequencies. Utilizing a two-dimensional (2D) mapping strategy, we then compute the visual distortion and file size increment (FSI) for each image block by examining non-zero alternating current (AC) coefficient pairs (NZACPs) and their corresponding run lengths. Finally, we select appropriate block groups based on the influential metrics of each block group and proceed with data embedding by 2D histogram shifting (HS). Extensive experimentation demonstrates how our method's efficiently and consistently outperformed existing techniques with a superior peak signal-to-noise Ratio (PSNR) and optimized FSI.

Histogram shifting based reversible data hiding with multiple expansion bin pairs

As the most popular reversible data hiding (RDH) technique, histogram shifting (HS) is widely investigated due to its efficient capacity-distortion trade-off. With a given histogram, the performance of HS-based RDH mainly depends on the selection of expansion bins. However, the determination of expansion bins, especially for the case of multiple pairs of expansion bins, usually involves massive solution space and is computationally expensive. To efficiently optimize the embedding performance, in this paper, the HS-based RDH with multiple expansion bin pairs (EBPs) is theoretically investigated, and some general results about the optimal EBPs are firstly derived. Based on the theoretical results, we conclude that a part of optimal EBPs can be directly determined without any computation burden for adaptive embedding. Then, inspired by the derived results, a simple yet efficient method to find the nearly optimal EBPs is proposed with a fairly low computational cost. Finally, the proposed method is further extended to multiple histograms based embedding for better capacity-distortion behavior. Experimental results show that the proposed method achieves better performance than some state-of-the-art works, especially in terms of computational cost.

Reversible data hiding in encrypted image with secure multi-party for telemedicine applications

Privacy protection of electronic patient information (EPI) is a crucial concern in the telemedicine applications, especially when it comes to communication between patients and doctors. In this paper, we propose a high-capacity reversible data hiding (RDH) algorithm combined with secure multi-party computation for telemedicine applications. The pixel values within a 3 × 3-size image block are divided into embedded pixels (EPs) and sampled pixels (SPs), secret data is embedded into the prediction errors of EPs and SPs in two stages. In the first stage, two edge servers predict EPs using the four SPs in the block to obtain the prediction error of EPs. The secret data is embedded into EPs through addition operation and histogram shift method. In the second stage, the prediction error of SPs is calculated as the difference between EPs and SPs, and the embedding of secret data follows the same method as used in the first stage. The experimental results demonstrate that the proposed algorithm achieves a higher embedding rate compared to classical and state-of-the-art algorithms, with an average embedding rate of 0.47bpp on the UCID dataset. Furthermore, the proposed algorithm exhibits robustness against existing attacks.

High-Payload Data-Hiding Scheme Based on Interpolation and Histogram Shifting

The whole world is quickly transforming into an information world with the setup of smart cities, among other things. Information and other forms of privacy are therefore at risk due to the growing intelligence of hackers. Therefore, to enhance the security of information, a capacity-raising interpolation method based on histogram shifting is proposed. According to the experimental results, the capacity-raising interpolation method based on histogram shifting produces high-quality images with a significantly high embedding capacity, outperforming other methods. Apart from the already-mentioned advantages, the proposed method is reversible and can recover images in a lossless manner.

Color image ROI encryption algorithm based on a novel 4D hyperchaotic system

The significance of safeguarding the security of image information has escalated significantly, owing to the exponential proliferation of digital images containing sensitive information being disseminated on the Internet. In this paper, we first propose a novel 4D hyperchaotic system and design a new image encryption algorithm in conjunction with the hyperchaotic system. The algorithm uses a split random swap permutation method to permute the image and combines the S-box to diffuse the image. To improve the diffusivity of this encryption algorithm, a cross-random diffusion method is designed to diffuse the image again. Then, we propose a region of interest (ROI) encryption scheme for images. This scheme can automatically identify irregular privacy targets in images and encrypt them. To ensure the security of the region of interest location information during transmission, the scheme compresses the location information of the privacy target using a run-length encoding technique and then embeds the compressed data into the ciphertext image using reversible steganography based on histogram shift. The experimental results and security analysis unequivocally demonstrate that the image encryption algorithm proposed in this paper exhibits robust resistance against a wide array of attacks, thereby ensuring a high level of security. Additionally, the devised image ROI encryption scheme effectively safeguards diverse privacy targets.

Enhancing Data Hiding Methods for Improved Cyber Security Through Histogram Shifting Direction Optimization

Enhancing Data Hiding Methods for Improved Cyber Security Through Histogram Shifting Direction Optimization

Quality guided reversible data hiding with contrast enhancement

Reversible data hiding with contrast enhancement (RDH-CE) enables contrast enhancement of the image and lossless recovery of host image after embedded data extraction. Although the current RDH-CE methods are effective in enhancing carrier images and ensuring reversibility, image quality control is still lacking, which may lead to poor image visualization. In more serious situations, the image detail information may be lost. The peak pair selection mechanism plays an extremely crucial role in image quality enhancement; however, existing work often neglects this. In this paper, we propose an RDH-CE algorithm that pursues high image quality maintenance, aiming to maintain image quality by guiding and controlling the selection of peaks and the direction of histogram shift through quality metrics. Specifically, instead of simply selecting only one pair of peaks at each peak selection stage, our algorithm selects several pairs of peaks that meet the requirements as candidate peak pairs. It then first performs simulated histogram equalization on these candidate peaks, followed by analyzing the situation of the quality metrics generated by each pair of peaks accordingly, and then selects the optimal peaks for the current iteration based on the performance of each pair of candidate peaks to be used in the equalization process. Our selection mechanism also dynamically changes the peak selection strategy based on before-and-after changes in the actual metrics, which can better ensure image quality stability over the iterations. Under the evaluations of a series of image quality metrics, the proposed method can still maintain high quality and visual effect under high iteration and has strong stability for image quality control, thus demonstrating the effectiveness and superiority of the proposed method.

High-Embedded Low-Distortion Multihistogram Shift Video Reversible Data Hiding Based on DCT Coefficient

Video reversible data hiding technology can be applied to copyright protection, medical images, the military, and other fields, but it cannot guarantee high visual quality with an effective embedded capacity. In this paper, a high-embedding and low-distortion reversible data hiding scheme based on a discrete cosine transform (DCT) coefficients method is proposed. The scheme first decodes the original video stream with entropy, obtains all the DCT blocks, and selects the embeddable DCT blocks according to the capacity of the zero factor. Then, it divides the coefficients in the DCT blocks into the shift and embedding coefficients. The shift coefficients directly generate a one-dimensional histogram; the embedding coefficients generate a two-dimensional histogram according to paired strategies. Finally, the secret data can be successfully embedded according to the proposed two-dimensional histogram shift reversible data hiding scheme. This scheme performed more effectively than existing schemes in terms of the embedded capacity, peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM).

Synthetic CT in Carbon Ion Radiotherapy of the Abdominal Site.

The generation of synthetic CT for carbon ion radiotherapy (CIRT) applications is challenging, since high accuracy is required in treatment planning and delivery, especially in an anatomical site as complex as the abdomen. Thirty-nine abdominal MRI-CT volume pairs were collected and a three-channel cGAN (accounting for air, bones, soft tissues) was used to generate sCTs. The network was tested on five held-out MRI volumes for two scenarios: (i) a CT-based segmentation of the MRI channels, to assess the quality of sCTs and (ii) an MRI manual segmentation, to simulate an MRI-only treatment scenario. The sCTs were evaluated by means of similarity metrics (e.g., mean absolute error, MAE) and geometrical criteria (e.g., dice coefficient). Recalculated CIRT plans were evaluated through dose volume histogram, gamma analysis and range shift analysis. The CT-based test set presented optimal MAE on bones (86.03 ± 10.76 HU), soft tissues (55.39 ± 3.41 HU) and air (54.42 ± 11.48 HU). Higher values were obtained from the MRI-only test set (MAEBONE = 154.87 ± 22.90 HU). The global gamma pass rate reached 94.88 ± 4.9% with 3%/3 mm, while the range shift reached a median (IQR) of 0.98 (3.64) mm. The three-channel cGAN can generate acceptable abdominal sCTs and allow for CIRT dose recalculations comparable to the clinical plans.

Reversible information hiding scheme based on interpolation and histogram shift for medical images.

Medical imaging and information management systems require transmission and storage of medical images over the Internet. Many reversible information hiding schemes for image have been proposed to ensure security and availability. In order to avoid the risk of medical information leakage and the medical image distortion, a reversible information hiding scheme based on interpolation and histogram shift for medical images has been proposed in this paper. The proposed adaptive interpolation between neighbor pixels (AIA) technique is used to obtain seed and non-seed pixels, which ensures the reversibility of the scheme while balancing the embedding capacity and the quality of marked image. Then, the image is divided into the region of interest (ROI) and the region of non-interest (NROI). Sensitive information such as electronic patient records (EPR) and electronic signatures of medical images are embedded as secret information. In the ROI, the corresponding bit histogram shift repeated embedding method (CBHSR) is adopted for embedding information to effectively avoid the problem of image distortion caused by histogram stretching. Experimental results show that algorithm not only has high embedding capacity, but also keeps the peak signal-to-noise ratio above 50dB, visual information fidelity and structural similarity above 0.99, which has good subjective and objective image quality.

JPEG Reversible Data Hiding Using Dynamic Distortion Optimizing With Frequency Priority Reassignment

JPEG Reversible data hiding (RDH) aims at correctly extracting hidden data and perfectly recovering the original JPEG image. By combining different frequency selection and block ordering manners, appropriate AC coefficients can be determined to achieve an efficient JPEG RDH scheme using histogram shifting (HS) mechanism. Nevertheless, existing methods only rely on rough frequency band distortion model by involving all DCT blocks, ignoring the dynamic influence of block ordering on embedding block selection. As a result, JPEG image containing hidden data may have an obvious distortion and file size expansion due to an inappropriate frequency band selection. To address the aforementioned problem, we design a new JPEG RDH scheme to optimize distortion model with frequency priority reassignment. Our method firstly orders all DCT blocks by referring to zero AC coefficients of each block. Subsequently, based on the given secret data, only partial ordered blocks are selected to calculate the distortion of each DCT frequency, which can further optimize unit distortion caused by shiftable coefficients. Furthermore, we employ the adjustment parameter to adaptively reassign the priority of each frequency band based on its influence on file size expansion, which can significantly reduce file size expansion of the marked JPEG image. Extensive experiments demonstrate that our method outperforms existing JPEG RDH methods with better visual quality and smaller file size increment of the marked image.

Sand-Dust Image Enhancement Using Chromatic Variance Consistency and Gamma Correction-Based Dehazing.

In sand-dust environments, the low quality of images captured outdoors adversely affects many remote-based image processing and computer vision systems, because of severe color casts, low contrast, and poor visibility of sand-dust images. In such cases, conventional color correction methods do not guarantee appropriate performance in outdoor computer vision applications. In this paper, we present a novel color correction and dehazing algorithm for sand-dust image enhancement. First, we propose an effective color correction method that preserves the consistency of the chromatic variances and maintains the coincidence of the chromatic means. Next, a transmission map for image dehazing is estimated using the gamma correction for the enhancement of color-corrected sand-dust images. Finally, a cross-correlation-based chromatic histogram shift algorithm is proposed to reduce the reddish artifacts in the enhanced images. We performed extensive experiments for various sand-dust images and compared the performance of the proposed method to that of several existing state-of-the-art enhancement methods. The simulation results indicated that the proposed enhancement scheme outperforms the existing approaches in terms of both subjective and objective qualities.

Trace Concealment Histogram-Shifting-Based Reversible Data Hiding with Improved Skipping Embedding and High-Precision Edge Predictor (ChinaMFS 2022)

Reversible data hiding (RDH) is a special class of steganography, in which the cover image can be perfectly recovered upon the extraction of the secret data. However, most image-based RDH schemes focus on improving capacity–distortion performance. In this paper, we propose a novel RDH scheme which not only effectively conceals the traces left by HS but also improves capacity–distortion performance. First, high-precision edge predictor LS-ET (Least Square predictor with Edge Type) is proposed, and the predictor divides pixels into five types, i.e., weak edge, horizontal edge, vertical edge, positive diagonal edge, and negative diagonal edge. Different types of target pixels utilize different training pixels with stronger local consistency to improve accuracy. Then, a novel prediction-based histogram-shifting (HS) framework is designed to conceal embedding traces in the stego images. Finally, we improve both the data-coding method and the skipping embedding strategy to improve the image quality. Experimental results demonstrate that the capacity–distortion performance of the proposed scheme outperforms the other trace concealment schemes and is comparable to the state-of-the-art schemes utilizing sorting technique, multiple histogram modification, and excellent LS-based predictors. Moreover, it can conceal the embedding traces left by the traditional HS schemes to a certain extent, reducing the risk of being steganalyzed.

Adaptive multi-histogram reversible data hiding with contrast enhancement

Unlike existing reversible data hiding with contrast enhancement (RDHCE) methods, which excessively improve the image contrast for achieving the required capacity, the proposed method improves the image contrast appropriately while providing satisfactory embedding capacity. To this end, an adaptive multi-histogram RDHCE method is proposed in this study to improve the local and global contrast by considering the local properties of the histograms. On the one hand, fuzzy C-means clustering combining multiple features that are deliberately designed for contrast enhancement is employed to generate seven sharply-distributed prediction error histograms (PEHs). Subsequently, the genetic algorithm is utilized to adaptively select the optimal pairs achieving the best embedding performance for each PEH according to the local characteristics of PEH distribution, resulting in improving the local contrast adaptively and embedding significant amount of data. Additionally, two-sided histogram shifting (HS) is utilized to improve the global contrast appropriately while embedding reasonable amount of data. The experimental results demonstrate that the proposed method achieves better local and global contrast while providing a high embedding capacity compared with other existing RDHCE methods.

Research on reversible image steganography of encrypted image based on image interpolation and difference histogram shift

In view of the mutual restriction between the image encryption and information hiding process in the ciphertext domain reversible algorithm, the existing ciphertext domain image reversible image steganography algorithm can hardly take into account the performance of security and hiding capacity. This paper proposes a reversible image steganography algorithm for encrypted images based on a new interpolation image and difference histogram shift. The pixel position of the carrier image is changed through the double scrambling operation of random image block recombination and Arnold transform. The chaotic sequence generated by logistic mapping is used as the diffusion sequence of the encryption algorithm, and DNA encoding and calculation are performed respectively to obtain the final encrypted image. According to the newly proposed interpolation algorithm, the encrypted image is enlarged, determine the pixel value of the target interpolated image according to the pixel value of the encrypted image, calculate the difference between the three adjacent pixel values of the target interpolated image, and use the difference histogram translation algorithm to convert the secret Information is embedded in an interpolated image, and only one bit of the pixel value can be modified in a single embedding process, and the embedding method is simple and effective. Experimental results show that under the premise of ensuring information security and image quality, the embedding capacity has also been greatly improved, reaching 1,572,864 bits. When the embedding rate is 0.67 bpp, the PSNR can also reach 58 dB and the image quality is maintained better.

Video-based Data Hiding by Exploring Prediction Error and Histogram Shifting

Video-based Data Hiding by Exploring Prediction Error and Histogram Shifting

Multiple Histograms Shifting-Based Video Data Hiding Using Compression Sensing

With the development of multimedia editing technologies, the copyright protection has attacked more attentions. Reversible data hiding (RDH), in which the cover can be recovered losslessly, is an effect method to eliminate embedding distortions. As a typical RDH method, histogram shifting (HS) is used widely. Most existing RDH schemes based on HS usually build sharp histograms by predicting and sorting techniques. To make use of spatial correlations of multimedia, several RDH schemes based on multiple HS (MHS) are proposed to protect copyright, in which some rigid rules are used to build multiple histograms. Against images, videos have more spatial and temple correlations and it is easier to acquire sharper histograms. In this paper, a video MHS scheme based on compression sensing (CS) is proposed. As a linear sensing algorithm, CS can measure macroblock residuals by reducing corrections among pixels to acquire distinguishable macroblock features, while keeping their statistical characteristics immutable. By employing CS, macroblocks with similar characteristics cluster together to formulate multiple histograms. For each of these histograms, data embedding is implemented to reduce shifting distortions by expanding the outermost bins while other bins are unchanged. Experimental results show that the quality of most test videos in our scheme are higher than that in the state-of-art schemes.

Digital Watermarking Scheme for Securing Textual Database Using Histogram Shifting Model

Information security is one of the most important methods of protecting the confidentiality and privacy of internet users. The greater the volume of data, the more the need to increase the security methods for protecting data from intruders. This task can be challenging for researchers in terms of managing enormous data and maintaining their safety and effectiveness. Protection of digital content is a major issue in maintaining the privacy and secrecy of data. Toward this end, digital watermarking is based on the concept of information security through the insertion and detection of an embedded watermark in an efficient manner. Recent methodologies in the research on digital watermarking result in data distortion after embedding the watermark. This scenario can reduce the efficiency of detecting watermarks as well as violate data confidentiality. This study adapts a robust reversible histogram shifting (HS) technique for textual data in relational databases. Furthermore, the study presents a digital watermarking scheme intended for document copyright protection and proof of ownership. The major advantage of the proposed watermarking scheme is that it can protect digital data and preserve data quality. To the best of our knowledge, this research methodology is promising for use in the HS expansion model for watermarking data on non-numeric databases. In addition, the results showed that despite exposing the database to an insertion attacks at 50% and 75% of the watermark, the proposed algorithm can recover more than half of the embedded watermark in all addition and alteration attacks cases. As a result, the watermark information can be retained and restored completely.