Pixel Value Ordering Research Articles

High-fidelity reversible data-hiding technique in encrypted images using pixel value ordering and center folding strategy

High-fidelity reversible data-hiding technique in encrypted images using pixel value ordering and center folding strategy

Global pixel-value-ordering framework with dynamic sequence partition for reversible data hiding

Recently, pixel-value-ordering (PVO) based reversible data hiding (RDH) methods have become hotspots in spatial domain RDH research. In PVO-based methods, the pixel value correlation within a local region is exploited for data embedding. To embed secret data, the cover images are partitioned into non-overlapped rectangle blocks to be sorted locally, and a lower embedding distortion is thus obtained. However, their embedding capacities are often limited by the fixed local blocks from which pixels are sorted. To maintain the advantages and overcome disadvantages of the PVO-based methods, a new global pixel-value-ordering (GPVO) framework is proposed in this paper. By applying our GPVO framework, a PVO-based method can utilize pixels from any position in the cover image rather than from local pixel blocks. Then, through the dynamic sequence partition realized by the GPVO framework for the first time, the embedding capacity of PVO-based methods can be greatly improved while reducing embedding distortion further reduced. Finally, we propose a two-stage pairwise embedding scheme to be applied to the sequences, aiming to achieve an advanced embedding performance. Experimental results illustrate that the proposed GPVO works better than other related state-of-the-art RDH methods.

Reversible data hiding using morphology based pixel classification

Pixel value ordering (PVO) prediction has become a research hotspot in reversible data hiding community and existing achievements have proved the necessity of predicting more pixels in a block. The increase in predicted pixels is beneficial to fully exploiting the redundancy of the cover image, but a single reference pixel may lead to decreased prediction quality. Given this situation, this paper proposes to develop PVO in correlated pixels construction and introduce morphology as an important theoretical tool. Several largest pixels in a block are first selected as candidate reference pixels which are to be activated in descending order. Except for the last one, each active reference pixel is used to predict all pixels to generate a correlation matrix, on which the dilation operation is carried out to pick out connected pixels that are likely to be strongly correlated with the reference pixel. Whether to activate the next reference pixel depends on the presence of disconnected pixels. Once the last reference pixel is activated, all remaining pixels will be taken as connected pixels. After the first round of embedding ends, several smallest pixels are similarly selected as candidate reference pixels to realize the second round of embedding. Experimental results demonstrate that satisfactory superiority in fidelity is achieved, e.g., the average PSNR for the Kodak image database is 63.85 dB with an embedding capacity of 10,000 bits.

PVO-Based Reversible Data Hiding Using Global Sorting and Fixed 2D Mapping Modification

Pixel-value-ordering (PVO) is one of the most popular methods in reversible data hiding (RDH). In PVO based methods, pixels are processed in a block-wise way, so that the local similarities of the images are considered but the global statistical characteristics are ignored. To better utilize the correlations of pixels, this paper proposes a global sorting strategy to combine utilizations of local and global characteristics of the images. For each pixel, its prediction value and local complexity are first calculated based on its local characteristics. Then the image pixels are sorted globally according to their prediction values to generate a single-sorted pixel sequence, in which the pixels with the equal prediction values are sorted again by referring to their local complexities. In such a way, the spatial distances of image pixels are broken so that the global statistical characteristics can be well exploited. With the proposed sorting strategy, we can obtain a more regular 2D histogram by segmenting the sorted sequence for the location-based PVO (LPVO) predictor. Owe to the regular 2D histogram, we have designed an efficient 2D mapping to achieve perfect performance for all the tested images. With the proposed RDH scheme, the PSNR of the image Lena is as high as 61.86 dB and the average PSNR of the Kodak dataset reaches 63.55 dB after embedding 10,000 bits. The superiority of the proposed method has been verified by comparing with recent state-of-the-art RDH methods.

Dynamic Hybrid Reversible Data Hiding Based on Pixel-value-ordering

<p>Reversible data hiding (RDH) using pixel-value-ordering (PVO) is a well-established technique for embedding data in a cover image by modifying the maximum and minimum in each block. This paper proposes a dynamic hybrid RDH method based on PVO. Specifically, a 3×3 block according to its complexity and two thresholds T1 and T2 is classified as three levels: extremely smooth, smooth, and rough. Different processing algorithms are used for different levels. For rough blocks, they are ignored to avoid reducing the peak signal-to-noise ratio. For smooth blocks, the proposed method employs a block subdivision algorithm that can embed up to 6 bits of data. For extremely smooth blocks, no subdivision is done and a median pixel prediction algorithm is used to predict the remaining eight pixels, which can embed up to 8 bits of data. Moreover, this paper presents a new method that computes complexity by dynamically selecting relevant pixels to enhance block classification accuracy. Extensive experiments demonstrate that the proposed method outperforms existing PVO-based methods, offering larger embedding capacity while maintaining low distortion.</p> <p> </p>

PVO-based reversible data hiding using bit plane segmentation and pixel expansion

The existing pixel value ordering (PVO)-based reversible data hiding (RDH) approaches have been investigated; however, the functions of these techniques are weak when embedded in rough image regions. Therefore, a novel PVO-RDH method is proposed herein. This method performs ‘bit-plane segmentation low bit-plane weighted-pixel expansion’ on an image, which improves the original PVO-RDH method’s embedding performance in rough image regions. Pixel expansion increases the number of image chunks, thereby improving the maximum embedding capability of the carrier image. After preprocessing, the expanded picture is chunked using the original PVO-RDH strategy, and the maximum value in the chunk is modified via prediction. In contrast to the results of previous studies, the current experimental findings indicate that the proposed strategy improves the average PSNR by at least 2.97 dB when the embedding data are 10000 bit. The experimental findings show a significant improvement in the embedding volume and PSNR of the proposed algorithm.

Reversible data hiding in encrypted image using two-pass pixel value ordering

In recent years, reversible data hiding in encrypted images (RDHEI) has gained popularity as it enables the concealing of confidential information in anonymized host image. Because of this, conventional reversible data hiding (RDH) methods that utilize the spatial correlation of natural host images cannot be employed in RDHEI where the embedding is done in the encrypted image without reserving a room. To address this concern, this paper proposes a new RDHEI method based on popular pixel value ordering (PVO) strategy of the conventional high fidelity RDH. For this, the image pixels are first encrypted in blocks using novel difference-preserving encryption for maintaining the original difference among the pixels of each block. Next, a two-pass PVO (TPPVO) is employed for embedding. The TPPVO first sorts the pixels and then performs the embedding in two passes in such a way that high embedding capacity (EC) can be obtained while also ensuring reversibility. Experimental evaluations also demonstrate that the proposed TPPVO-RDHEI not only enlarges the EC approximately by 0.1130 bpp and 0.01 bpp for the smooth and complex images, respectively in comparison to the best performing related method, but also maintain the visual security while ensuring the complete reversibility.

An adaptive pixel value ordering based reversible data hiding scheme for images

Pixel value ordering (PVO) is a prominent method for reversible data hiding, which has gained increasing research interest in recent years due to its effective performance producing high-fidelity marked images. PVO-based methods performance lies on block dimensions and thresholds for classification. However, most methods determine proper parameters by performing exhaustive searches in restricted search spaces. In this paper, we present a novel Pixel Value Ordering technique based on Genetic Algorithms (PVO-GA) to address these issues. PVO-GA uses flexible sized blocks to take advantage of different block features, to better exploit image redundancy. A genetic algorithm is introduced to optimize the embedding process since the solutions space is greatly expanded. Results showed that PVO-GA achieves satisfactory levels in terms of the introduced distortion while significantly reduces the computational cost needed to find the optimal set of insertion parameters when compared to previous methods.

Lossless Reversible Data Hiding in Encrypted Image for Multiple Data Hiders Based on Pixel Value Order and Secret Sharing

Reversible data hiding in encrypted images (RDH-EI) is instrumental in image privacy protection and data embedding. However, conventional RDH-EI models, involving image providers, data hiders, and receivers, limit the number of data hiders to one, which restricts its applicability in scenarios requiring multiple data embedders. Therefore, the need for an RDH-EI accommodating multiple data hiders, especially for copyright protection, has become crucial. Addressing this, we introduce the application of Pixel Value Order (PVO) technology to encrypted reversible data hiding, combined with the secret image sharing (SIS) scheme. This creates a novel scheme, PVO, Chaotic System, Secret Sharing-based Reversible Data Hiding in Encrypted Image (PCSRDH-EI), which satisfies the (k,n) threshold property. An image is partitioned into N shadow images, and reconstruction is feasible when at least k shadow images are available. This method enables separate data extraction and image decryption. Our scheme combines stream encryption, based on chaotic systems, with secret sharing, underpinned by the Chinese remainder theorem (CRT), ensuring secure secret sharing. Empirical tests show that PCSRDH-EI can reach a maximum embedding rate of 5.706 bpp, outperforming the state-of-the-art and demonstrating superior encryption effects.

An Information Security Method Based on Optimized High-Fidelity Reversible Data Hiding

With the rapid development of the Industrial Internet of Things, the social and commercial value of digital information is greatly increased, and the sensitive information of groups and individuals faces more and more problems. Due to providing accurate prediction errors, pixel value ordering (PVO)-based reversible data hiding is an active research topic. In this article, a new PVO-based embedding method is proposed first, in which the adaptive data embedding is adopted according to the difference between the maximum/minimum three pixels. Second, a pixel collecting and sorting model is designed to increase expandable prediction errors. By converting a shifting prediction error into an expandable one, the proposed PVO-based embedding method creates conditions for data embedding. In this way, the space location of pixels in the block can be better utilized to achieve improved capacity-distortion performance. Finally, an adaptive embedding is designed based on the content of the target block and its neighboring blocks. By measuring the complexity of the target pixel block, smoothing blocks are preferentially used and the appropriate embedding is determined by the complexity. The experimental results show that the proposed method outperforms a series of PVO-based multipass embedding methods. The proposed method is expected to address some security issues in the industrial and privacy fields, such as enhancing confidentiality and security during the transmission of sensitive information.

Reversible Data Hiding Using an Improved Pixel Value Ordering and Complementary Strategy

Reversible data hiding (RDH) schemes based on pixel value ordering have gained significant popularity due to their unique capability of providing high-quality marked images with a decent embedding capacity, while also enabling secret information extraction and the lossless recovery of the original images at the receiving side. However, the marked image quality may be distorted severely when the pixel value ordering (PVO) method is employed in a layer-wise manner to increase the embedding capacity. In this paper, a new high-capacity RDH scheme using a complementary strategy is introduced to overcome the limitation of the image quality in the case of layer-wise embedding. The proposed RDH scheme first divides the cover image into non-overlapping blocks of 2 × 2 pixels uniformly and then sorts the pixels of each block according to their intensity values. The secret data are then embedded into two layers. In the first layer, the minimum value of the block is decreased and the maximum value is increased by either 1 or 2 to embed the secret data bits. The second layer is used as a complement to the first layer and is in symmetry with the first layer. In the second layer, the proposed RDH scheme increases the minimum valued pixel and decreases the maximum valued pixel in order to minimize the distortion resulting from the first layer embedding and to embed an additional amount of the secret data. As a result, the proposed RDH scheme significantly increases the embedding capacity, which is clearly evident from the conducted experimental results.

Reversible data hiding for color images based on pixel value order of overall process channel

Color image Reversible Data Hiding (RDH) is getting more and more important since the number of its applications is steadily growing. This paper proposes an efficient color image RDH scheme based on pixel value ordering (PVO), in which the channel correlation is fully utilized to improve the embedding performance. In the proposed method, the channel correlation is used in the overall process of data embedding including prediction stage, block selection and capacity allocation. In the prediction stage, since the pixel values in the co-located blocks in different channels are monotonically consistent, the large pixel values are collected preferentially by pre-sorting the intra-block pixels. This can effectively improve the embedding capacity of RDH based on PVO. In the block selection stage, the description accuracy of block complexity value is improved by exploiting the texture similarity between the channels. The smoothing the block is then preferentially used to reduce invalid shifts. To achieve low complexity and high accuracy in capacity allocation, the proportion of the expanded prediction error to the total expanded prediction error in each channel is calculated during the capacity allocation process. The experimental results show that the proposed scheme achieves significant superiority in fidelity over a series of state-of-the-art schemes. For example, the PSNR of the Lena image reaches 62.43 dB, which is a 0.16 dB gain compared to the best results in the literature with a 20,000bits embedding capacity.

Local feature-based mutual complexity for pixel-value-ordering reversible data hiding

In reversible data hiding, pixel-value-ordering (PVO) has become a widely used framework benefiting from its high-fidelity under low-capacity requirements. As an essential element in PVO-based methods, complexity could effectively avoid embedded images from unnecessary embedding distortions. There are two main context-pixel-selection strategies in existing complexity methods: inside-block and outside-block pixel selection strategy, which show strong complementarity. To make full use of this characteristic and further mitigate the insufficient feature representation problem in complexity, we propose a multi-complexity mechanism: Mutual Complexity. First, we analyse the relationship between complexity and Capacity-Distortion performance and innovatively regard the complexity problem as a binary classification problem. Then, precision and recall are taken as the optimization objectives and mutual pixels with the best performances could be selected. As a result, our proposed method can leverage different local features represented by various complexities and obtain the best classification result. Furthermore, to solve the block-dependent embedding problem in existing complexities, a simple but effective complexity, named Neighbor Complexity, is designed according to pixel location information. Experimental results show that Mutual Complexity could be easily generalized to different PVO-based methods and the embedding distortions are all effectively controlled.

Multi-dimensional constraints-based PPVO for high fidelity reversible data hiding

Reversible data hiding (RDH) based on pixel value ordering (PVO) is an effective information security hiding technique that allows complete recovery of the cover image after data extraction. Current methods mainly adopt PVO-based prediction error histograms to obtain better embedding performance in terms of block prediction or pixel prediction, regardless of the correlation between image textures and different prediction contexts. In this work, we propose a multi-dimensional constraints (MDCs) method to explore prediction mechanism across three dimensions using a pixel-based PVO (PPVO) scheme for high-fidelity RDH. The method involves (a) a local complexity (LC) Constraint that focuses on pixels containing similar local complexity instances with more different quantization intervals, (b) an enclosing location (EL) constraint that discovers the most relevant pixels within the fully enclosed context for prediction error, and (c) an optimal mode (OM) constraint that fuses fully enclosed context adaptively based on optimal search during different subsets of prediction modes. In addition, we introduce the multi-dimensional constraints to determine the prediction context in the PPVO-based RDH method, which helps the prediction mechanism to obtain predictions that are more accurate. The proposed MDCs can be easily applied to existing PPVO-based RDH frameworks. Extensive experiments on test-image data sets show that the proposed method outperforms the current state-of-the-art PPVO approaches.

Flexible patch moving modes for pixel-value-ordering based reversible data hiding methods

Pixel-value-ordering (PVO) is one of the most popular frameworks in the research of reversible data hiding (RDH) in recent years. In most PVO-based methods, the cover image is divided into non-overlapped blocks to embed secret data into the maximum and minimum pixels of each block. In these methods, the image division processing is just like moving a patch with constant step lengths in both horizontal and vertical directions. As a result, the block amount, as well as the embedding capacity (EC), is limited by the image size and the patch size. In this paper, we propose one-dimensional and two-dimensional flexible patch moving (FPM) modes to move the patch with adaptive step lengths. After enlarging the EC by employing FPM, an average-difference-based complexity computation is also proposed to cooperate with FPM to further improve the embedding performance. Finally, multiple pairwise embedding schemes are adaptively applied on blocks with different complexities to enhance the embedding performance once again. Experimental results illustrate that the proposed FPM modes work well on many PVO-based methods. Moreover, the proposed method achieves an obvious improvement in fidelity when compared with some state-of-the-art RDH schemes.

A Novel Revesible Data Hiding based on Improved Pixel Value Ordering Method

Reversible data hiding based on the preservationof sorted pixel value ordering (PVO) technique has beenresearched and expanded recently due to its high embeddingcapacity and good image quality. secret data is always embedded in the largest or smallest pixel of the sub-block. Thus,each sub-block will embed two bits. The more blocks an imagehas, the higher the embedding potential. In this paper, to havemore sub-blocks, the paper divides the image into sub-blockswith 3 pixels and embeds 2 bits in the maximum value and1 bit in the minimum value. Therefore, each sub-block of theproposed embedding scheme is able to hide three bits insteadof the two bits as in the original scheme. The experimentalresults also show that the proposed scheme has a significantlyhigher embedding capacity.

Weighted Average-Based Complexity Calculation in Block Selection Oriented Reversible Data Hiding

Complexity of blocks is one of the key factors to influence the performance of reversible data hiding. By embedding data into blocks with smaller complexity, the invalid shifting in pixel value ordering (PVO) based reversible data hiding is largely reduced, thus increasing the imperceptibility. However, the existing techniques are not always accurate in complexity calculation, since context pixels are usually treated equally. As a result, a great number of invalid pixels are assigned too large a weight in complexity calculation, which inevitably affects the precision. In this paper, we propose a weighted average based novel strategy in complexity calculation. In this scheme, we calculate the weight of a certain context pixel with the help of its adjacent neighbors, which ensures the one in a smooth area is assigned a larger weight, and otherwise the opposite. Experimental results show that invalid shifting is largely reduced, thus our scheme is better in imperceptibility compared with the state of the art and has potential application in a lot of fields such as medical imaging.

Reversible data hiding with dual pixel-value-ordering and minimum prediction error expansion.

Pixel Value Ordering (PVO) holds an impressive property for high fidelity Reversible Data Hiding (RDH). In this paper, we introduce a dual PVO (dPVO) for Prediction Error Expansion (PEE), and thereby develop a new RDH scheme to offer a better rate-distortion performance. Particularly, we propose to embed in two phases: forward and backward. In the forward phase, PVO with classic PEE is applied to every non-overlapping image block of size 1 × 3. In the backward phase, minimum-set and maximum-set of pixels are determined from the pixels predicted in the forward phase. The minimum set only contains the lowest predicted pixels and the maximum set contains the largest predicted pixels of each image block. Proposed dPVO with PEE is then applied to both sets, so that the pixel values of minimum set are increased and that of the maximum set are decreased by a unit value. Thereby, the pixels predicted in the forward embedding can partially be restored to their original values resulting in both a better embedded image quality and a higher embedding rate. Experimental results have recorded a promising rate-distortion performance of our scheme with a significant improvement of embedded image quality at higher embedding rates compared to the popular and state-of-the-art PVO-based RDH schemes.

Reversible Data Hiding Based on Multichannel Difference Value Ordering for Color Images

Considering the correlation existing among different color channels of the image, we propose a novel reversible data hiding (RDH) scheme based on multichannel difference value ordering. By applying the technique of pixel value ordering (PVO), we establish a certain relationship by sorting the differences among adjacent pixels of different color channels. Combined with the optimized pairwise prediction-error expansion by analyzing the distribution of pixel difference, our scheme can adaptively select pixel channel to achieve reversible embedding of information. Through simulation experiments, the feasibility of this RDH scheme in image distortion and computational complexity is verified. When the threshold T is set to 3, the average PSNR of the test images embedded with 5000 bits of additional information in each color channel can reach 63.41, 64.38, and 63.96, respectively. When the embedding capacity is 20000 bits, the PSNR of the color image can reach 58.90 dB. Considering the embedding capacity and image distortion comprehensively from the simulation data, our proposed scheme has a better performance than the previous PVO-RDH schemes which only consider a single channel.

AN EFFICIENT REVERSIBLE DATA HIDING BASED ON IMPROVED PIXEL VALUE ORDERING METHOD

Pixel value ordering (PVO) has been considered as an effective reversible data hiding method for high embedding capacity and good imperceptibility. In this paper, we propose a novel reversible data hiding based on four sorted pixels. This paper proposes a new reversible hiding scheme based on the arrangement of 4 pixels. Three bits will be embedded into each four pixels sub block without changing the order while the original PVO method only embeds 2 bits. In case the amount of payload is less than the embedding capacity, flatter blocks will be prioritized for embedding to improve image quality. To determine the flat of block, we use 12 neighborhood pixels of current block. Only blocks with satisfactory flatness are used for embedding. The proposed reversible data hiding not only gains high capacity but also gets good imperceptibility. Experimental results also show that the proposed reversible data hiding scheme outperforms several widely schemes using pixel value ordering method in terms of both image quality and embedding capacity.