A novel reversible image data hiding scheme based on pixel value ordering and dynamic pixel block partition

This work presents a novel histogram-based reversible data hiding scheme. Although common histogram-based reversible data hiding schemes can achieve high image quality, embedding capacity is restricted because general images usually do not contain a great number of pixels with the same pixel values. To improve embedding capacity and retain low distortion, the proposed scheme uses prediction-error values, which are derived from the difference between an original image and a predictive image, instead of using the original pixels to convey a secret message. In the proposed scheme, a predictive image is generated using the mean interpolation prediction method. Since the obtained predictive image is very similar to the original image, prediction-error values are to be tended to zero. That is, a great quantity of peak points gathers around zero. The proposed scheme takes full advantage of this property to increase embedding capacity and retain slight distortion. Moreover, a threshold is used to balance the tradeoff between embedding capacity and image quality, i.e. embedding capacity in the proposed scheme is scalable. Furthermore, only a threshold is needed to record, not a large amount of information of peak and zero points, when high embedding capacity is required. Additionally, a multilevel mechanism is employed to further increase embedding capacity. Experimental results indicate that the proposed scheme is superior to other reversible schemes in terms of both image quality and embedding capacity.

Pixel Value Ordering (PVO) is a data hiding technique through which pixels within an image block are ranked and modify minimum or maximum pixel value for data embedding. In this paper, we proposed an Extended Directional Improved PVO (EDIPVO) for Reversible Data Hiding (RDH) scheme. The original image pixel blocks are interpolated by inserting extra pixels between each row and each column. During data embedding, we consider a parameter \((\alpha )\), perform addition with maximum pixel value, and subtract \(\alpha \) from minimum pixel value to maintain the order within the pixel block. The parameter \(\alpha \) is dependent on the size of the original image block. To increase data hiding capacity, we have considered pixel ranking in three different directions: (1) horizontal, (2) vertical, and (3) diagonal within each block. The secret data bits are embedded starting from the largest, second largest, and so on for maximum value modification and smallest, second smallest, and so on for minimum value modification. The data embedding capacity has been improved than previous data hiding schemes while keeping visual quality unaltered. Experimental results are compared with existing state-of-the-art methods and achieve good results.

Enhanced pairwise IPVO-based reversible data hiding scheme using rhombus context

With the increasing number of video applications, it is essential to resolve issues such as ineffective search of video content, tampered/forged video content, packet loss, to name a few. Data embedding is typically utilized as one of the solutions to address the aforementioned issues. One of the important requirements of data embedding is to maximize embedding capacity with minimal bit rate overhead while ensuring imperceptibility of the inserted data. However, embedding capacity varies depending on the video content and increasing the embedding capacity usually leads to video quality degradation. In this work, a threshold-controlled block splitting technique is proposed for embedding data into SHVC video. Specifically, the embedding capacity can be increased by coding the host video by using more small blocks, which can be achieved by tuning a threshold-controlled parameter in the rate distortion optimization process. Subsequently, the predictive syntax elements in both intra and inter-coded blocks are jointly utilized to embed data, which ensures that data can be embedded regardless of the prediction mode used in coding a block. Results suggest that the proposed method can achieve a trade-off between the increase in embedding capacity and bit rate overhead while maintaining video quality. In the best case scenario, the sequence PartyScene can embed 516.9 kbps with an average bit rate overhead of +7.0% for the Low Delay P configuration, while the same video can embed 1578.6 kbps with an average bit rate overhead of +2.9% for the All Intra configuration.

Pixel clustering is a technique of content-adaptive data embedding in the area of high-performance reversible data hiding (RDH). Using pixel clustering, the pixels in a cover image can be classified into different groups based on a single factor, which is usually the local complexity. Since finer pixel clustering seems to improve the embedding performance, in this manuscript, we propose using two factors for two-dimensional pixel clustering to develop high-performance RDH. Firstly, in addition to the local complexity, a novel factor was designed as the second factor for pixel clustering. Specifically, the proposed factor was defined using the rotation-invariant code derived from pixel relationships in the four-neighborhood. Then, pixels were allocated to the two-dimensional clusters based on the two clustering factors, and cluster-based pixel prediction was realized. As a result, two-dimensional prediction-error histograms (2D-PEHs) were constructed, and performance optimization was based on the selection of expansion bins from the 2D-PEHs. Next, an algorithm for fast expansion-bin selection was introduced to reduce the time complexity. Lastly, data embedding was realized using the technique of prediction-error expansion according to the optimally selected expansion bins. Extensive experiments show that the embedding performance was significantly enhanced, particularly in terms of improved image quality and reduced time complexity, and embedding capacity also moderately improved.

The pixel-based pixel value ordering (PPVO) which each pixel is predicted using its sorted context pixels greatly improves the embedding capacity compared with the traditional PVO algorithm. In the PPVO algorithm, the prediction value is more accurate if there is a strong correlation between the target pixel and its context pixels. Otherwise, the prediction value is inaccurate. For higher embedding capacity and image quality, an improved PPVO is proposed. In the proposed scheme, the target pixel is predicted by a specific prediction method first. Then, the predicted value is added to the context pixels as the additional pixel value. In the process of embedding data, the target pixel is predicted by using the maximum / minimum value of the context pixel vector. Experimental results demonstrate that the embedding capacity of proposed method is larger than that of PPVO. The marked image quality of proposed method is also better than that of PPVO.

Self-scheduling is a method for task schedul ing in parallel programs, in which each processor acquires a new block of tasks for execution whenever it becomes idle. To get the best performance, the block size must be chosen to balance the scheduling overhead against the load im balance. To determine the best block size, better analytical models of self-scheduling are needed. We present an experimental study of self-scheduling on a BBN TC2000, a NUMA machine. Previously published models to predict running time and optimal block size were tested using our experimental results. Although the models gave good predictions for small block sizes, for large block sizes the models fail, underestimating the running time by almost a factor of two. We present an upper bound on the running time and use it to explain this failure.

An adaptive steganographic method based on the measurement of just noticeable distortion profile

Data Hiding is now a major area of study due to the internet’s and multimedia technologies’ amazing growth. Since data digitization and communication networking have advanced over the past ten years, protecting data transmitted over the internet has become a major concern. Numerous applications in steganography and watermarking find it desirable when data is integrated into multimedia cover content, such as audio, video, and photos. Watermarking will be used for copyright protection, while steganography will be chosen for encrypted communication. Even when the embedded data has been extracted, irreparable harm to the cover media is usually unavoidable with such techniques. The perfect restoration of the cover medium is necessary for some applications, including remote sensing, military mapping, geographic mapping, and medical diagnostics. Therefore, in sensitive applications like medical diagnosis, reversible data hiding techniques are used to restore the cover media without distortion. Confidential information, the content owner’s identity, cover content details, etc., are examples of the data that may be included. The data hiding techniques will be shown in order to determine the trade-off between embedding capacity and visual quality. The frequency coefficients of the cover image are changed by the frequency domain techniques. According to experimental results, the recommended procedures perform better than the current approaches for standard QCIF encoded videos and standard grayscale images utilizing the USC SIPI image database. The suggested method considers the information of the cover content during data embedding and provides a trade-off between visual quality and embedding capacity.

Improved Pixel-Value-Ordering (I-PVO) based reversible data hiding scheme provides high-fidelity watermarked images with high data embedding capacity. In this paper, a new reversible data hiding scheme based on the I-PVO method is proposed to increase the data embedding capacity. The proposed scheme first divides the cover image into non-overlapping blocks of size 2 × 2 pixels uniformly, and then increases and decreases the values of highest and lowest pixels by one respectively. The proposed modification rules are used to reserve two bins which are used to increase the data hiding capacity. The pixels are sorted in ascending order to calculate two differences in the block. The difference values are calculated by considering pixel values along with their locations in the original block and are utilized to embed secret data in the highest and lowest pixel values using the proposed modification rules. While embedding the secret data, the pixel values are either increased/decreased or left unchanged according to the embedding bits. Thus, the proposed scheme embeds three bits per block in the cover image instead of two bits in the case of I-PVO on average. The experimental results show that the proposed scheme provides significantly higher data hiding capacity than the previous works.

The characteristic basis function method (CBFM) was developed as an algorithm to accelerate the analysis based on direct matrix solver. In CBFM, the optimum block size greatly influences the overall analysis time. In this paper, we propose a guideline for setting the optimal block size that can be applied to every algorithm based on CBFM. Rough estimation of the optimal block size is L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rough</sup> ≈ 0.05N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6/29</sup> , and the exact optimal block size can be determined through preprocessing based on rough estimation. Numerical results of electromagnetic scattering problems are shown to demonstrate the performance of the proposed method.

For multifocus image fusion in spatial domain, sharper blocks from different source images are selected to fuse a new image. Block size significantly affects the fusion results and a fixed block size is not applicable in various multifocus images. In this paper, a novel multifocus image fusion algorithm using biogeography-based optimization is proposed to obtain the optimal block size. The sharper blocks of each source image are first selected by sum modified Laplacian and morphological filter to contain an initial fused image. Then, the proposed algorithm uses the migration and mutation operation of biogeography-based optimization to search the optimal block size according to the fitness function in respect of spatial frequency. The chaotic search is adopted during iteration to improve optimization precision. The final fused image is constructed based on the optimal block size. Experimental results demonstrate that the proposed algorithm has good quantitative and visual evaluations.

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.

This study introduces a reliable and reversible image steganography framework that integrates Radon Transform (RT) and Integer Lifting Wavelet Transform (ILWT) to enhance security, imperceptibility, and robustness. The RT obscures data locations through rotations, scaling, and translations, while ILWT ensures full reversibility by maintaining integer wavelet coefficients, guaranteeing no data loss during reconstruction. Data embedding is performed in the middle bit planes of high-frequency sub-bands (LH, HL, HH) using Arithmetic Coding, optimizing space utilization while preserving visual quality. Experimental results demonstrate the method’s superior performance, achieving Peak Signal-to-Noise Ratio (PSNR) values exceeding 46 dB, a 15% improvement over existing techniques, ensuring the hidden data remains imperceptible to the human eye. The system exhibits exceptional robustness, with a Bit Error Rate (BER) as low as 0.0017 under scaling distortions, representing a 30% reduction compared to state-of-the-art methods. Additionally, the framework achieves a 25% increase in embedding capacity, enabling higher data payloads without compromising image quality. High Normalized Correlation Coefficient (NCC) values close to 1 further confirm the algorithm’s reversibility and integrity, even under common attacks such as Gaussian Noise, Poisson Noise, Blurring, and Cropping. Compared to existing techniques, the proposed framework demonstrates significant improvements in embedding capacity, robustness, and visual fidelity. This method holds great promise for secure data hiding in applications such as secure communication, medical imaging, and digital watermarking, where data integrity and confidentiality are critical.

Reversible watermarking has drawn extensive attentions in recent years due to its broad applications of digital forensics and data security. This paper proposes a novel reversible watermarking approach, which has the perfect reversibility of the embedded data and the original image. In order to increase the embedding capacity, the proposed approach utilizes the enhanced local prediction to reduce the prediction error of every pixel value. Different from traditional reversible watermarking algorithms considering all the image pixels equivalently before prediction, an enhanced image is first computed by multiplying the original image with its saliency map. By linearly formulating each pixel value in an original image as a weighted sum of the enhanced pixel values in its local neighborhood, the correlation coefficients as learned weights are then solved as a least squares solution. Based on two state-of-the-art datasets, the experimental results show that the proposed approach achieves large embedding capacity with relatively low visual distortion.