Multiple Histograms Modification Research Articles

A Survey on Reversible Data Hiding for Uncompressed Images

Reversible data hiding (RDH) has developed various theories and algorithms since the early 1990s. The existing works involve a large amount of specialized knowledge, making it difficult for researchers, especially primary learners, to have a good grounding in the basic ideas. In this survey, we will review the mainstream RDH algorithms in uncompressed images and analyze their unique features to provide readers with an introduction to basic topics in RDH. We analyze the most effective RDH frameworks and their common extensions. The classic techniques, including lossless compression-based RDH, difference expansion, integer transform, histogram shifting, prediction-error expansion (PEE), and their extensions, will be reviewed first. Then, three currently popular investigated schemes, i.e., multiple histograms modification, pairwise PEE, and pixel-value-ordering, are presented in detail. Four aspects of these mainstream techniques are reviewed and analyzed, including the evolution of embedding frameworks, detailed technological features, extensions, and the current state of the art. Furthermore, we look forward the possible future research based on early-age motivations.

General Expansion-Shifting Model for Reversible Data Hiding: Theoretical Investigation and Practical Algorithm Design

As a specific data hiding technique, reversible data hiding (RDH) has recently received extensive attention. By this technique, both the embedded data and the original cover image can be exactly extracted from the marked image. In our previous work, a general expansion-shifting model for RDH is proposed by introducing the so-called reversible embedding function (REF). With REF, RDH can be designed and the corresponding rate-distortion formulations can be established, providing an approach to optimize the reversible embedding performance. In this paper, by extending our previous work, optimal REF for one-dimensional histogram is investigated, and all optimal REF are derived in this case when the maximum modification to the cover pixel is limited as a small value. Moreover, based on the derived optimal REF for one-dimensional histogram and multiple histograms modification, a practical RDH scheme is presented and it is experimental verified better than some state-of-the-art algorithms in terms of capacity-distortion performance.

Grayscale-Invariant Reversible Data Hiding Based on Multiple Histograms Modification

Grayscale-invariant reversible data hiding (GI-RDH) in color images is a data embedding framework in which the grayscales of a marked color image must be identical to those of the host color image. Recently, some state-of-the-art GI-RDH schemes were proposed. However, their performance in embedding distortion is unsatisfactory. In order to obtain better image quality, a well-known histogram-shifting-based RDH method called multiple histograms modification (MHM) is considered. In this paper, we propose an MHM-based GI-RDH scheme. First, we modified our previous GI-RDH scheme using a multiple-histogram-shifting approach instead of a difference expansion approach. Next, we designed a procedure to select expansion–bin pairs for generated histograms to achieve low embedding distortion through further data embedding. Specifically, we analyzed the expected embedding distortion of our MHM-based GI-RDH scheme given any set of expansion–bin pairs. We then formulated an optimization problem called the GI-MHM minimization problem to identify the optimal expansion–bin pairs for further embedding tasks. Finally, we generated an approximated solution for the GI-MHM minimization problem and conducted the embedding task with these selected expansion–bin pairs. The experimental results revealed that the proposed GI-RDH scheme outperformed previous methods when the embedding capacity was small.

Multiple Histograms-Based Reversible Data Hiding Using Fast Performance Optimization and Adaptive Pixel Distribution

Abstract In prediction error-based reversible data hiding, multiple histograms modification (MHM) is well known for high image quality and thus has received wide attention in recent years. However, the computational cost for performance optimization in MHM is too high, which is particularly critical for real-time applications. This manuscript aims to reduce the computational complexity of MHM by presenting two techniques, including fast performance optimization and adaptive pixel distribution. Fast performance optimization provides a two-stage process for optimal bin selection by exploiting the concept of per-bit distortion of data embedding within a prediction error histogram (PEH). In fast performance optimization, the distribution characteristics of the per-bit distortion are investigated to significantly narrow down the solution space of optimal bin selection. The second technique is adaptive pixel distribution, which tries to nonuniformly allocate pixels into multiple PEHs to further reduce the time complexity. Extensive experiments show that the computational complexity of MHM is significantly reduced while well preserving the image quality.

Human Visual System Guided Reversible Data Hiding Based On Multiple Histograms Modification

AbstractIn this paper, we propose a human visual system (HVS) guided reversible data hiding method based on multiple histograms modification. The proposed method utilizes the texture features of an image to adaptively modify the pixels, for a lower HVS quality distortion. The HVS quality is taken as the optimization objective and a new expansion-bin-selection strategy is given to solve the optimization. For the same HVS quality distortion, the proposed method can embed more secret bits and give higher priority for the modifications in texture regions. Besides, a better optimization rule is proposed to accelerate the speed and then consider more available solutions. In this way, a trade-off between the embedding performance and time complexity can be achieved. Experimental results show that the proposed method can achieve a better HVS quality than the conventional methods.

Fast Expansion-Bins-Determination for Multiple Histograms Modification Based Reversible Data Hiding

Reversible data hiding (RDH) is a research hotspot nowadays. By RDH, after data extraction, the cover image can be restored without information loss. Among numerous existing RDH techniques, multiple histograms modification (MHM) is a general reversible embedding framework, and it is experimentally verified better than the traditional single histogram based methods. However, the expansion-bins-determination process for MHM is conducted through naive exhaustive search, which is time consuming. Based on this consideration, a fast expansion-bins-determination method for MHM is proposed in this paper. Specifically, to determine the optimal expansion bins, instead of solving the optimization problem of discrete variables, we consider a general form of this problem with differentiable objective function and real variables, so that advanced analysis tools such as Lagrange multiplier can be utilized. By the proposed approach, compared with the original MHM, the expansion bins can be determined quickly with only a tiny performance loss, and thus the practicality of MHM is improved.

Multiple histogram based adaptive pairwise prediction-error modification for efficient reversible image watermarking

The prediction-error expansion (PEE) based reversible data hiding (RDH) methods mainly contain two steps: prediction-error histogram (PEH) generation and PEH modification. Multiple histograms modification (MHM) and pairwise PEE are two efficient methods used in PEH modification, which can greatly reduce the distortion to the cover image under given payloads. However, these two effective methods are not used together for their conflict on causality and large computational complexity. To address the above two problems, in this paper, a group of non-causal predictors with a specially designed complexity metric are proposed so that the conflict on causality is resolved. Then, the original optimization problem to determine the parameters of MHM and pairwise PEE is decomposed into two easier ones, which are addressed by a step-by-step optimization. Several efficient constraints are used to narrow the search range for the optimal parameters while minimizing the performance degradation. As a result, the proposed method can obtain high quality on the embedded images, that is, the average PSNR of the 8 standard 512 × 512 sized grayscale images from the USC-SIPI image database can reach 60.43 dB for the payload of 10,000 bits, while the running time for the proposed algorithm is acceptable. Experimental results show that the proposed method outperforms many kinds of RDH schemes, and achieves an obvious increment in fidelity over a series of state-of-the-art RDH schemes.

Reversible Data Hiding Based on Adaptive Multiple Histograms Modification

Pixel value ordering prediction has been verified as an effective mechanism to exploit image redundancy for reversible data hiding (RDH) and numerous extensions have been devised. However, their performance is still unsatisfactory since the error modification is generally fixed and independent of image content. In this paper, a new RDH scheme is proposed by incorporating pixel distance to realize adaptive multiple histograms modification (AMHM). During exploiting the correlation between the largest/smallest pixel and any other one in the scope of pixel block, we propose to process every two correlated pixels successively following the ascending order of their distance. Specifically, the generated errors with a given distance are collected and verified. If they are all shiftable errors, the follow-up errors would be collected into the next sub-histogram. In this way, a histogram sequence is adaptively generated such that different modification mechanisms can be taken for different sub-histograms to achieve adaptive embedding. Finally, AMHM for conventional prediction-error expansion (PEE) and AMHM for 2D PEE have been both realized in this paper. Experimental results show that AMHM is of great significance to better exploit pixel correlation and the proposed scheme outperforms a series of the latest schemes.

Reversible data hiding based on multiple histograms modification and deep neural networks

In the previous multiple histograms modification (MHM) based reversible data hiding (RDH) method, the prediction-error histograms are generated by a fixed manner, which may constrain the performance owing to the lack of adaptivity. In order to compensate this, we propose a deep neural networks (DNN) based method for dynamical multiple histograms generation. Through learning the prior knowledge, DNN is able to establish the histograms with different sizes for a better redundancy exploitation. For each histogram, two optimal expansion bins will be determined to minimize the distortion caused by the modification. Besides, the strategy consisted of the memo technique and the entropy measurement are applied to accelerate the parameter optimization. Experimental results show that the proposed method outperforms some of state-of-the-art RDH methods.

An improved reversible watermarking scheme using weighted prediction and watermarking simulation

For the reversible watermarking scheme using the prediction error expansion and histogram shifting (PEE–HS), improving the prediction accuracy facilitates performance enhancement, which still remains a challenging problem in this field. To this end, the paper improves the state-of-the-art local predictor (LP) by designing the following approaches: 1) enlarging the prediction context; 2) partitioning the prediction block surrounding the target pixel into the watermarked and original regions, and imposing different weights on prediction values from these two regions to generate the final prediction for the target pixel; and 3) conducting watermarking simulation on the original region via random noises to further enhance the prediction performance. These three approaches are then integrated to result in an improved LP using weighted prediction and watermarking simulation (LP-WPWS). By exploiting the LP-WPWS for prediction error generation, we thus construct a new PEE–HS-based reversible watermarking scheme. Extensive simulation shows that the proposed scheme outperforms the state-of-the-art LP and is comparable to the excellent methods exploiting the sorting, multiple histograms modification, and hybrid dimensional histogram generation with adaptive mapping selection.

Optimal Reversible Data Hiding Scheme Based on Multiple Histograms Modification

Recently, a method based on multiple histograms modification (MHM) is proposed for reversible data hiding (RDH), in which a sequence of prediction-error histograms are generated and two expansion bins are selected in each histogram for expansion embedding. However, although efficient, it only chooses a single pair of expansion bins which limits the embedding capacity. On the other hand, the exhaustive expansion-bin-selection procedure in MHM takes huge computation time, so that it cannot be extended for high capacity RDH. In order to overcome the aforementioned drawbacks, an optimal RDH scheme based on MHM for high capacity embedding is proposed in this paper. First, to improve the embedding capacity, instead of a single pair of expansion bins, multiple pairs of expansion bins are utilized for each histogram, and the multiple-expansion-bin-selection for optimal embedding is formulated as an optimization problem. Then, unlike the exhaustive searching way used in MHM, a computationally efficient algorithm is proposed to solve the optimization problem, so that the optimal expansion bins can be adaptively determined to optimize the embedding performance. By the proposed approach, high embedding capacity can be achieved with good marked image quality, and the experimental results show that it is better than the original MHM and some other state-of-the-art methods.

High Capacity Reversible Data Hiding Based on Multiple Histograms Modification

Among various techniques of reversible data hiding (RDH), histogram modification has been mostly investigated and used in practice. Recently, a new histogram modification technique, namely, multiple histograms modification (MHM) has been proposed and received the increasing attentions. By MHM, multiple histograms can be modified differently to achieve an excellent embedding performance. However, as the maximum modification on each pixel value is one, only one pair of bins in the prediction-error histogram (PEH) is allowed to be expanded for data embedding. This drawback leads to a low capacity and limits its extension for high capacity scenario. To remedy this, we extend the MHM scheme and propose an efficient solution for the high-capacity embedding. Specifically, instead of only selecting one pair of expansion bins in each PEH, in the proposed method, multiple pairs of bins are utilized for expansion in each PEH. Moreover, the embedding performance is optimized by an advisable expansion bin selection strategy. Consequently, the multiple PEHs are allowed to embed with different capacities according to the sharpness of distribution, i.e., the low-index PEH that consists of more smooth pixels is embedded with a larger capacity than the high-index one. Besides, a simplified parameter determination is developed to seek the optimal solution with a good computational efficiency. The experimental results show that the proposed method can achieve the high capacity and provide a better performance than some state-of-the-art RDH methods.

Multiple Histograms-Based Reversible Data Hiding: Framework and Realization

Reversible data hiding (RDH) has unique advantage in copyright and integrity protection for multimedia contents. As a typical RDH scheme, histogram shifting technique (HS) has found wide applications due to its high quality of marked image. At present, most existing HS-based RDH schemes rely on single histogram generated from cover image to hide data. Since the single histogram-based approach (SH_RDH) commonly employs smooth regions in the cover image for data hiding, it might not well utilize the cover image and exploit the correlations among image contents of different texture characteristics. In this paper, a novel RDH general framework using multiple histograms modification (MH_RDH) is proposed, which involves two key issues as follows: 1) the construction of multiple histograms based on optimized multi-features and 2) the rate allocation among multiple histograms is formulated as the one of rate-distortion optimization and solved with evolutionary algorithms. The experimental results show that the proposed method could considerably increase the payload of current MH_RDH-based embedding (ranging from 0.2 to 0.7 bpp for most test images) and outperform the other state-of-the-art SH_RDH and MH_RDH schemes.

Reversible Data Hiding Based On Multiple Histograms Modification

Reversible data hiding (RDH) is utilized to embed secret message into a cover image by somewhat adjusting its pixel values. Embedded message and the spread image are totally recuperated from the checked substance. RDH underpins data hiding with the lossless compressibility of normal images. Lossless pressure, distinction expansion, histogram change, predection error expansion and whole number change systems are utilized for RDH process. Histogram based RDH method is isolated into two stages histogram era and histogram change. Histogram development is performed with the pixel pair's arrangements and their diverse qualities. Histogram change is done to insert data into the spread image. The unhiding procedure recuperates the message furthermore the spread image. Predection error expansion (PEE) procedure is connected for Reversible Data Hiding (RDH) process. Maybe a couple dimensional Prediction-error Histogram (PEH) are utilized as a part of the PEE systems. The two-dimensional PEH-based methods perform superior to anything one dimensional PEH. PEH alteration is settled and free of image substance. Various histograms based PEE method is received to enhance reversible data hiding (RDH) process. Numerous Histograms Modification (MHM) method utilizes an arrangement of histograms for the hiding procedure. A multifaceted nature estimation is processed for every pixel with reference to its setting. Predection Error Histogram (PEH) is produced utilizing the pixels with the multifaceted nature esteem. A grouping of histograms can be produced by differing the multifaceted nature to cover the entire image. Two expansion containers are chosen in each produced histogram. The expansion containers are chosen with reference to the image content. Data installing is done on Multiple Histograms Modification (MHM). Index Terms: Reversible data hiding, prediction-error expansion, multiple histograms modification, adaptive embedding, Zhicheng's Method.

Improved PVO-based reversible data hiding: A new implementation based on multiple histograms modification

Pixel-value-ordering (PVO) technique is based on ranking the pixels within a block and often employed in reversible data hiding (RDH) to preserve the image fidelity. In this paper, an improved PVO-based algorithm is proposed to achieve a high-performance RDH. We formulate the PVO embedding as a problem of multiple histograms modification (MHM) and make two contributions. Firstly, the existing PVO-based algorithms are generalized in an MHM form and the systematic analysis of performance is offered by considering the parameter determination. Secondly, based on the new framework, an improved implementation of PVO embedding is proposed to adaptively determine the MHM manner by solving the distortion minimization of multiple histograms, which can handle different characteristics in image areas. Experimental results show the superiority of the new algorithm over the state-of-the-art methods.

Efficient Reversible Data Hiding Based on Multiple Histograms Modification

Prediction-error expansion (PEE) is the most successful reversible data hiding (RDH) technique, and existing PEE-based RDH methods are mainly based on the modification of one- or two-dimensional prediction-error histogram (PEH). The two-dimensional PEH-based methods perform generally better than those based on one-dimensional PEH; however, their performance is still unsatisfactory since the PEH modification manner is fixed and independent of image content. In this paper, we propose a new RDH method based on PEE for multiple histograms. Unlike the previous methods, we consider in this paper a sequence of histograms and devise a new embedding mechanism based on multiple histograms modification (MHM). A complexity measurement is computed for each pixel according to its context, and the pixels with a given complexity are collected together to generate a PEH. By varying the complexity to cover the whole image, a sequence of histograms can be generated. Then, two expansion bins are selected in each generated histogram and data embedding is realized based on MHM. Here, the expansion bins are adaptively selected considering the image content such that the embedding distortion is minimized. With such selected expansion bins, the proposed MHM-based RDH method works well. Experimental results show that the proposed method outperforms the conventional PEE and its miscellaneous extensions including both one- or two-dimensional PEH-based ones.