Prediction-error Expansion Research Articles

Selective Weighting and Prediction Error Expansion for High-Fidelity Images

Selective Weighting and Prediction Error Expansion for High-Fidelity Images

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.

A reversible natural language watermarking for sensitive information protection

Existing methods have evolved from using synonym substitution to incorporating arbitrary word substitution to achieve reversible natural language watermarking. However, a notable limitation is that they are prone to overlook the sensitivity of information associated with the original words, with a tendency to prefer non-sensitive words for substitution. As a result, a potential risk of sensitive information leakage contained in the original text is posed. Furthermore, while aiming for reversibility, the overall performance of the watermarking method may be inadvertently compromised. In response to the above problems, this paper puts forward a novel reversible natural language watermarking method that combines a Keyword Substitution scheme and a Prediction Error Expansion algorithm (KSPEE) to protect sensitive information, verify content integrity, protect copyright, and so on. Specifically, KSPEE leverages a keyword extraction algorithm to identify important content containing sensitive information in the original text, thereby determining the potential positions for watermark information embedding. Subsequently, a masked language model is utilized to predict appropriate substitution words based on the surrounding semantic information of the embedding position. In addition, the prediction error expansion algorithm is employed to select appropriate words for substituting the original keywords, ensuring the successful embedding of watermark information while maintaining the recoverability of the original keywords. By identifying keywords and substituting them, a suitable method of protecting the original sensitive information is provided. Extensive experiments demonstrate that, under the promise of semantic distortion and lossless restoration of the original content, the proposed method KSPEE achieves outstanding watermarked text quality. A higher watermark embedding rate is achieved and strong security is shown by KSPEE. More importantly, KSPEE effectively prevents the leakage of sensitive information.

Adaptive Reversible 3D Model Hiding Method Based on Convolutional Neural Network Prediction Error Expansion

Although reversible data hiding technology is widely used, it still faces several challenges and issues. These include ensuring the security and reliability of embedded secret data, improving the embedding capacity, and maintaining the quality of media data. Additionally, irregular data types, such as three-dimensional point clouds and triangle mesh-represented 3D models, lack an ordered structure in their representation. As a result, embedding these irregular data into digital media does not provide sufficient information for the complete recovery of the original data during extraction. To address this issue, this paper proposes a method based on convolutional neural network prediction error expansion to enhance the embedding capacity of carrier images while maintaining acceptable visual quality. The triangle mesh representation of the 3D model is regularized in a two-dimensional parameterization domain, and the regularized 3D model is reversibly embedded into the image. The process of embedding and extracting confidential information in carrier images is symmetrical, and the regularization and restoration of 3D models are also symmetrical. Experiments show that the proposed method increases the reversible embedding capacity, and the triangle mesh can be conveniently subjected to reversible hiding.

A review of different prediction methods for reversible data hiding

In recent times, prediction error expansion (PEE) based reversible data hiding (RDH) schemes have gained significant traction due to their performance in terms of embedding capacity and image quality. However, the major part of their performance is dependent on how good the prediction has been. For a good prediction, various predictors such as median edge detection (MED), rhombus mean, least square, convolution neural network based predictor (CNNP) have been introduced. In this paper, a review of the working predictors being used in PEE-RDH is presented and discussed. In addition, a new predictor using extreme gradient boosting (XGBoost) is introduced in reversible data hiding. The XGBoost predictor makes use of a machine learning algorithm, where several optimization techniques are combined to get accurate results. To evaluate the performance comprehensively, experimental results considering different test images have been used and analyzed. From the analysis, it has been found that the XGBoost provides better prediction accuracy than some of the existing predictors. However, its performance is not up to the level of some other popular predictors such as least square, CNNP.

Colour image steganography through channel transformation approach

The interest in information security strategies is expanding due the quick expansion in the use of media content and transmission over the web employments. Accordingly, there is a need of special embedding procedure for steganography. The creator presents an original reversible data hiding (RDH) algorithm for colour pictures that further develops the inserting execution by applying a channel transformation function and a versatile expectation blunder extension prediction error expansion structure. The proposed calculation will bring the first colour picture with no information misfortune from the inserted picture (which is unique in addition to target picture). The evaluation is done here at the zero for the given base place for the histogram that marginally changes the pixel esteems for inserting the information. It can insert more information when contrasted with the majority of the current data hiding calculations. A hypothetical confirmation and various examinations state that the embedding capability of the proposed model which consistently is further noteworthy in comparison with other reversible data hiding techniques. The calculation has been applied to a wide scope of various colour pictures effectively. Some exploratory outcomes are introduced to show the legitimacy of the calculation.

Reversible Data Hiding With Pattern Adaptive Prediction

Abstract In the area of reversible data hiding (RDH), one of the most popular techniques is prediction-error expansion (PEE), which hides data in the prediction errors with well-preserved image fidelity. The key to a successful PEE-based RDH implementation usually lies in prediction algorithms with high accuracy. Existing PEE-based RDH works often employ one single prediction algorithm, which is usually globally optimized, but with less consideration of the pixel distribution characteristics within local neighborhoods. In this manuscript, the technique of pattern adaptive prediction is proposed for pixel estimation according to the type of local binary pattern (LBP), which is obtained from the pixel’s eight neighborhood. Theoretically speaking, pattern-based predictors can be designed for each and every LBP patterns to create multiple prediction-error histograms (PEHs). However, the process of performance optimization with multiple PEHs requires extremely heavy computing power. To speed up the optimization process, LBP patterns are classified into various groups based on the degree of histogram concentration. Experiments demonstrate that the prediction accuracy is obviously improved and the image fidelity is well preserved.

Reversible data hiding based on histogram and prediction error for sharing secret data

With the advancement of communication technology, a large number of data are constantly transmitted through the internet for various purposes, which are prone to be illegally accessed by third parties. Therefore, securing such data is crucial to protect the transmitted information from falling into the wrong hands. Among data protection schemes, Secret Image Sharing is one of the most popular methods. It protects critical messages or data by embedding them in an image and sharing it with some users. Furthermore, it combines the security concepts in that private data are embedded into a cover image and then secured using the secret-sharing method. Despite its advantages, this method may produce noise, making the resulting stego file much different from its cover. Moreover, the size of private data that can be embedded is limited. This research works on these problems by utilizing prediction-error expansion and histogram-based approaches to embed the data. To recover the cover image, the SS method based on the Chinese remainder theorem is used. The experimental results indicate that this proposed method performs better than similar methods in several cover images and scenarios.

Efficient reversible data hiding via two layers of double-peak embedding

Reversible data hiding continues to attract significant attention in recent years. In particular, an increasing number of authors focus on the higher significant bit (HSB) plane of an image which can yield more redundant space. On the other hand, the lower significant bit planes are often ignored for embedding in existing schemes due to their harm to the embedding rate. This paper proposes an efficient reversible data hiding scheme via a double-peak two-layer embedding (DTLE) strategy with prediction error expansion. The higher six-bit planes of the image are assigned as the HSB plane, and double prediction error peaks are applied in either embedding layer. This makes fuller use of the redundancy space of images compared with the one error peak strategy. Moreover, we carry out the median-edge detector pre-processing for complex images to reduce the size of the auxiliary information. A series of experimental results show that our DTLE approach makes better performance on the scenarios of high capacity embedding and achieves up to 83% higher embedding rate on real-world datasets, BOSSbase, BOWS2 and UCID, than the state-of-the-art approaches while guaranteeing better image quality.

Visually meaningful image encryption combining with reversible data hiding in encrypted images

At present, most image encryption schemes directly change plaintext images into ciphertext images without visual significance, such ciphertext images can be detected and therefore subject to various attacks. To protect the content security and visual safety of images, a visually meaningful image encryption (VMIE) algorithm combining with Reversible Data Hiding in Encrypted Images (RDHEI) is proposed. First, the secret image is encrypted and additional information is embedded by Chen hyperchaotic system and prediction error expansion (PEE) method. Then the source image is encrypted by a Lorentz hyperchaotic system. Finally, bit-xor operation is performed on the resulting encrypted image and the result is saved in a cloud database to generate an indexing key using SHA-256,which enables the receiver to recover the secret image with the resource image and the key alone. The scheme combines with the RDHEI, improving the transmission efficiency and has no image quality problems.The experimental results and security analysis show that the image encryption scheme has sufficient key space, strong key sensitivity and wide applicability.

Efficient Reversible Data Hiding Using Two-Dimensional Pixel Clustering

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.

Reversible data hiding based on prediction-error value ordering and multiple-embedding

Prediction-error expansion (PEE) is the most widely investigated reversible data hiding (RDH) framework. However, the performance of PEE can be further improved since the image redundancy has not fully exploited yet. To enlarge the embedding capacity and optimize the embedding performance of RDH, in this paper, a novel high-fidelity reversible embedding scheme using prediction-error value ordering and multiple embedding is proposed. Incorporating with PEE, the inter-correlation of prediction-errors is exploited by ordering the prediction-errors values, and multiple to-be-modified prediction-errors within each image block are adaptive determined. Specifically, firstly, a newly defined prediction-error is computed by considering the difference between the top two maximum (minimum) prediction-errors within each image block. Then, these prediction-errors are modified for expansion embedding. Moreover, a multiple-embedding mechanism is introduced to adaptive determine the number of modified prediction-errors. Finally, extensive experiments are conducted, and it is experimentally verified that the proposed method is superior to than some state-of-the-art RDH works. A benchmark result is that, for the standard Lena image, a PSNR as high as 62.01 dB can be derived for an embedding capacity of 10,000 bits.

A Novel Reversible Data Hiding Scheme Based on Pixel-Residual Histogram

Prediction-error expansion (PEE) is the most popular reversible data hiding (RDH) technique due to its efficient capacity-distortion tradeoff. With the generated prediction-error histogram (PEH) and adaptively selected expansion bins, the image redundancy is well exploited by PEE. However, for the most widely used rhombus predictor, the rounding operation which groups different prediction-errors into one value is completely unnecessary. The embedding can be extended to a general case by removing the rounding operation, and more histogram bins can be derived for expansion with a new mapping mechanism. Therefore, in this article, instead of pixel prediction-error, we propose to compute the pixel residuals without the rounding operation, and a new embedding mechanism based on pixel-residual histogram (PRH) modification is devised. In PRH, four bins correspond to one bin in PEH. Then, different from the one-to-one mapping between the prediction-error and pixel modification, a four-to-one mapping between the pixel-residual and pixel modification is established, and the performance is optimized by adaptively selecting four expansion bin pairs for embedding. Since more modification selections are considered, better performance can be obtained. Moreover, the proposed scheme is extended to the two-dimensional (2D) histogram and multiple histograms based embedding, and the performance is further enhanced. The superiority of the proposed method is experimentally verified by comparing it with some state-of-the-art works.

Reversible Data Hiding Method for Interpolated Images Based on Modulo Operation and Prediction-Error Expansion

Image reversible data hiding (RDH) using interpolation technology (IT) provides high single-layer embedding capacity and has become a research hotspot. To solve serious image distortion in existing methods caused by secondary embedding based on interpolated pixels, we propose an RDH method for interpolated images based on modulo operation and prediction-error expansion (PEE). Our method differs from existing RDH methods that only consider embedding data into interpolated pixels. First, an image interpolation method is used to generate an interpolated image, which is partitioned into overlapping 3 × 3 blocks. For each block, secret data are embedded into interpolated pixels using the modulo operation and into reference pixel using the PEE. Standard grayscale images of size 512 × 512 were selected from the popular BossBase and BOWS-2 image databases as input images for experiments. Extensive experimental results show that the proposed method avoids pixel values underflow/overflow caused by embedding data and reduces image distortion effectively. For each standard test image, the maximum bits per pixel (BPP) is higher than 1.5, and the corresponding peak signal to noise ratio (PSNR) is higher than 45.0 dB. At the given embedding capacity, the proposed method outperforms recent similar methods regarding the watermarked image quality. Moreover, the proposed method can resist histogram analysis, and the subjective evaluation proves that the proposed method has good imperceptibility.

A Reversible Data-Hiding Method with Prediction-Error Expansion in Compressible Encrypted Images

This paper proposes a novel reversible data-hiding method in encrypted images to achieve both a high hiding capacity and good compression performance. The proposed method can also decrypt marked encrypted images without data extraction, so marked images containing a payload can be derived from marked encrypted images. A perceptual encryption algorithm proposed for an encryption-then-compression framework is used to generate compressible encrypted images. In addition, a predictor with high accuracy and a prediction-error expansion and histogram shifting method are used for data hiding. Consequently, the proposed method can compress marked encrypted images without loss using image coding standards and achieve a high hiding rate. Experimental results show the effectiveness of the method in terms of hiding capacity or marked-image quality and lossless compression efficiency.

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.

Reversible Data Hiding for Color Images Based on Adaptive 3D Prediction-Error Expansion and Double Deep Q-Network

Reversible data hiding (RDH) for color images has attracted increasing attention in recent years. Due to its effective utilization of the correlation between prediction errors, high-dimensional prediction-error expansion (PEE) can achieve much better performance for color image RDH than low-dimensional PEE. However, existing studies only focus on high-dimensional PEE with nonadaptive embedding. To further improve the embedding performance for color images, we propose a novel three-dimensional PEE method that is adaptive to image content. Double deep Q-network (DDQN), introduced to RDH for the first time, is adopted to find the optimal mapping paths for PEE. In addition, an action selection scheme is presented for DDQN to efficiently find the reversible mapping paths. Extensive experiments show that the proposed method outperforms existing color image RDH methods in image quality.

Reversible Data Hiding Based on Multiple Adaptive Two-Dimensional Prediction-Error Histograms Modification

Pairwise prediction-error expansion (pairwise PEE) is an efficient reversible data hiding technique based on two-dimensional (2D) histogram modification. However, without considering the image content, its embedding manner lacks adaptivity. In this paper, we propose the adaptive modification for multiple prediction-error histograms (PEHs). An iterative self-learning optimization algorithm is devised to adaptively generate the 2D mapping, based on the PEH and payload. A loss function is employed and the optimization can be solved linearly to reduce the time cost. To determine the appropriate expansion bins, we generate multiple 2D PEHs and establish multiple candidate mappings. The optimal combination of the adaptive 2D mappings is determined by using exhaustive searching. Our method is flexible as the 2D mapping generation has a broad dynamic range. The experimental results show that the proposed method can outperform the pairwise PEE and also provide a competitive performance compared with the other state-of-the-art 2D RDH methods.

Reversible data hiding with pairwise PEE and 2D-PEH decomposition

Reversible data hiding (RDH) is an important topic of data hiding. In this paper, we exploit pixel-based pixel value ordering prediction (PPVO) and pairwise prediction-error expansion (PEE) to design a novel RDH with pairwise PEE and 2-dimensional prediction-error histogram (2D-PEH) decomposition. Specifically, for each pair of pixels, they are accurately predicted according to the maximum and minimum values of their neighbor pixels by using PPVO to generate a global 2D-PEH. After selecting negative 2D expansion bins, a series of sub 2D-PEHs can be obtained by decomposing the global 2D-PEH according to the errors between the maximum and minimum values of neighbor pixels. Finally, for each sub 2D-PEH, pairwise PEE mapping manner is selected to conduct data embedding according to the characteristic of the sub 2D-PEH. Experimental results demonstrate that the proposed method can achieve good capacity-distortion performance and outperforms some state-of-the-art RDH methods.