Prediction-error Expansion Research Articles

Greedy Pairing Prediction-error Expansion for Reversible Data Hiding

Reversible data hiding (RDH) is a method to embed messages in an image that human eyes are difficult to recognize the differences between the original image and the embedded image. But the method has to make sure that the original image and the embedded information can be exactly recovered. Then, prediction-error expansion (PEE) is a successful way to realize RDH. However, it is fixed when pairing the conventional 2D PEH. So, the capacities and embedding distortion is not ideal. In this paper, we propose a method called greedy pairing prediction-error expansion (GPPEE) based on the method called pairwise reversible data hiding in Ou 􀝁􀝐 􀜽􀝈.. GPPEE can achieve more efficient embedding and can improve the embedding distortion.

High-Fidelity Reversible Data Hiding Using Directionally Enclosed Prediction

Recently, a number of high-fidelity reversible data hiding algorithms have been developed based on prediction-error expansion (PEE) and pixel sorting. In PEE, prediction is made using either a full-enclosed or a half-enclosed predictor. While in PEE with pixel sorting, the local complexity (LC), which is usually assumed to be proportional to the magnitude of prediction-error (PE), is exploited to reduce the embedding distortion. However, this assumption may not always hold in all conditions. In this letter, a directional enclosed predictor is proposed to detect the locations where LC is not proportional to PE. And, a directionally enclosed prediction and expansion (DEPE) scheme is then developed for efficient reversible data hiding. With DEPE, data embedding is restricted to pixels where LC correlates to PE with a proportional relationship. Experimental results show that, compared to the full-enclosed or half-enclosed prediction schemes, DEPE significantly improves the image fidelity while providing a considerable payload.

Guided filtering based color image reversible data hiding

In this paper, a color-image-dedicated reversible data hiding (RDH) algorithm is proposed to improve embedding performance by applying a guided filtering predictor and an adaptive prediction-error expansion (PEE) scheme. PEE-based RDH methods can be mainly separated into two stages for each channel embedding, i.e., pixel prediction and prediction-error histogram (PEH) modification. In our work, the inter-channel correlation is exploited at all stages of prediction and modification. Specifically, for predicting the pixels in the current channel with the guidance of pixels from other channels, a linear transform model from reference channels to the current channel is established and its coefficients are determined by the Laplacian minimization criterion. Then, to modify the PEH, an adaptive PEE embedding scheme is conducted by seeking the optimal parameters of the embedding bins and the complexity threshold to minimize distortion. The experimental results demonstrate the proposed method has better performance than the state-of-the-art, color-image RDH methods.

High-fidelity reversible data hiding based on geodesic path and pairwise prediction-error expansion

It is often important for reversible data hiding (RDH) to reduce the amount of image modifications for a given capacity. To this end, recently, a pairwise prediction-error expansion (pairwise PEE) is proposed to better exploit the image redundancy in the two-dimensional (2D) space. However, in conventional pairwise PEE, a drawback is that the pixel pair is generated by a fixed combination manner, which may limit the further improvement of embedding performance. Based on this consideration, we propose a new histogram generation strategy for the 2D RDH by using geodesic path. In contrast to the fixed manner, the pixels are adaptively combined into pairs with respect to the local similarity in terms of both spatial distance and intensity. As a result, the prediction-errors in a pair are more correlated to each other and the derived 2D PEH is more advisable for pairwise PEE. Experimental results show that, the proposed method can reduce the embedding distortion of conventional pairwise PEE, and yields a superior performance than some state-of-the-art methods.

Reversible video watermarking through recursive histogram modification

In this study, a new reversible video watermarking method is developed. Unlike the existing reversible video watermarking algorithms that are based on motion compensated prediction or interpolation error expansion, motion compensated interpolation errors in the proposed method are watermarked in a totally different manner with the aid of recursive histogram modification algorithm developed recently for the purpose of reversible image watermarking. However,the recursive histogram modification can not be used directly for reversible video watermarking since the important problems of ensuring reversibility for each frame and distribution of total capacity among frames are encountered. In this study, novel ideas are proposed to solve the aforementioned two problems. The proposed method is tested on the video sequences commonly used in the literature. It is shown to give better performance than the existing reversible video watermarking algorithms in terms of capacity and distortion by means of computer simulations. Also, a numerical example is provided.

Reversible data hiding based on prediction error expansion using adjacent pixels

Reversible watermarking is a method of hiding the watermark in digital media in such a way that visually its effect is almost negligible and after extracting the watermark, digital media can be restored to its original form bit-by-bit. Reversible watermarking has applications in the fields that are very sensitive towards security such as defense, medical, legal matters, artwork, and so on. This paper investigates the concept of prediction error expansion in developing a fragile reversible data hiding technique for digital images. The fact that the adjacent pixels are highly correlated is exploited by the proposed technique for providing high embedding capacity along with good visual quality. The proposed technique utilizes odd columns to predict the values of even columns and embeds the watermark into even columns. Compressed location map is used to handle the overflow and underflow problem. Experimental results using various standard test images and a comparison with recent existing techniques show that the proposed technique provides high embedding capacity with better visual quality. Higher peak signal-to-noise ratio values indicate the effectiveness of the proposed technique. Copyright © 2016 John Wiley & Sons, Ltd.

Reversible data hiding based on an adaptive pixel-embedding strategy and two-layer embedding

Recently, Peng et al. proposed a reversible data-hiding (RDH) method based on pixel value ordering (PVO) and prediction-error expansion. In their method, the maximum and minimum of a pixel block are predicted and modified to embed data, and reversibility is guaranteed by keeping the PVO of each block invariant after embedding. In this paper, a novel RDH method is proposed by incorporating an adaptive pixel-modification strategy into Peng et al.’s work. In our method, the smoother a block, the more data it can carry. Unlike Peng et al.’s method, which embeds data uniformly, ours can embed data (e.g., 2, 4, or 6 bits) adaptively into a block according to the local complexity. Specifically, the local complexity is classified into four levels according to the strength of the correlation between each block and its neighborhood. Then the number of pixels to be modified is determined by the complexity level such that more bits are embedded into a block located in a smoother region. Moreover, two embedding layers are utilized together to further decrease the embedding distortion. Extensive experiments verify that the proposed method outperforms Penget al.’s, Ou et al.’s, Li et al.’s, Sachnev et al.’s, and Hong et al.’s works.

High-fidelity reversible data hiding based on pixel-value-ordering and pairwise prediction-error expansion

Pixel-value-ordering (PVO) technique refers to the process of first ranking the pixels in a block and then modifying the maximum/minimum for reversible data hiding (RDH). This paper discusses the PVO embedding in two-dimensional (2D) space and utilizes the prediction-error pair within a block for data embedding. We focus on not only the exploitation of conventional PVO embedding but also its effective implementation in 2D form. The PVO embedding is extended into a 2D form by integrating the pairwise prediction-error expansion, and a reversible 2D mapping adapted to the special distribution of prediction-error pairs is proposed. Moreover, an adaptive mapping selection mechanism is proposed to treat separately rough and smooth prediction-error pairs to further optimize the embedding performance. Experimental results show that the proposed method outperforms the previous PVO-based methods.

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.

Adaptive reversible video watermarking based on motion-compensated prediction error expansion with pixel selection

In this study, a motion-compensated prediction error expansion-based adaptive reversible video watermarking algorithm is proposed. Blocks of motion-compensated frames are classified as smooth and non-smooth according to their prediction errors. Unlike the current reversible video watermarking methods that apply a single watermarking strategy to all blocks, the proposed method uses two different strategies for smooth and non-smooth blocks. This adaptive strategy is shown to increase watermarking capacity. In addition, an approach is suggested to detect those pixels causing high distortion in the watermarked video and they are not used in watermarking to limit the distortion occurring in the original video. Simulations show that the proposed method is superior to existing methods in terms of capacity and distortion.

A Low Distortion Reversible Data Hiding Technique Using Improved PPVO Predictor

Reversible data hiding is a technique that embeds additional information into some distortion-unacceptable cover media, such as military images, in a reversible manner so that the original cover image can be restored after extraction of the hidden information. This work extends a recently proposed reversible data hiding (RDH) scheme of Qu et al. which is based on pixel-based pixel- value-ordering(PPVO) and prediction-error expansion. In Qu et al.’s method, each pixel is predicted using its sorted context pixels. In this work, the pixel neighborhood of each pixel is expanded to optimize the embedding performance in terms of capacity- distortion behavior. This can better exploit image redundancy; achieve superior embedding performance and low distortion. Thus, the proposed method is able to embed adequate data into a cover image with limited distortion. The superiority of this predictor is verified through extensive experimental results. The proposed method outperforms prior works in terms of PSNR. The PSNR of a modified image versus its original one is guaranteed to be above 57.0dB.

Hybrid Predictor and Field-Biased Context Pixel Selection Based on PPVO

Most pixel-value-ordering (PVO) predictors generated prediction-errors including −1 and 1 in a block-by-block manner. Pixel-based PVO (PPVO) method provided a novel pixel scan strategy in a pixel-by-pixel way. Prediction-error bin 0 is expanded for embedding with the help of equalizing context pixels for prediction. In this paper, a PPVO-based hybrid predictor (HPPVO) is proposed as an extension. HPPVO predicts pixel in both positive and negative orientations. Assisted by expansion bins selection technique, this hybrid predictor presents an optimized prediction-error expansion strategy including bin 0. Furthermore, a novel field-biased context pixel selection is already developed, with which detailed correlations of around pixels are better exploited more than equalizing scheme merely. Experiment results show that the proposed HPPVO improves embedding capacity and enhances marked image fidelity. It also outperforms some other state-of-the-art methods of reversible data hiding, especially for moderate and large payloads.

PEE based RW using fuzzy conditional entropy for image partitioning

Prediction error expansion (PEE) based reversible watermarking (RW) has found to be efficient for meeting the high embedding rate at low visual distortion. However, the existing works mostly use single predictor over the entire host image. Further performance improvement is possible using predictors based on local characteristics of the image. To this aim, this work first proposes a method to partition the image into different regions, namely the smooth, the texture and the edge regions using multiple thresholds on pixel gradients. The threshold values are calculated by maximizing the fuzzy conditional entropy of the gradient values. The optimal set of parameters for the fuzzy membership functions are specified by differential evolution method. Two predictors are then proposed, one for prediction of gray values in the edge region and the other one for the texture and the smooth region. RW is then done using region specific PEE. A large set of simulation results are shown to highlight its improved rate-distortion performance over the existing works followed by semi-fragile nature of watermark decoding against common operations like smoothing filtering, noise addition, cropping, random bending attack, etc.

최소자승 예측오차 확장 기반 가역성 DNA 워터마킹

최소자승 예측오차 확장 기반 가역성 DNA 워터마킹

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.

Non-integer expansion embedding techniques for reversible image watermarking

This work aims at reducing the embedding distortion of prediction-error expansion (PE)-based reversible watermarking. In the classical PE embedding method proposed by Thodi and Rodriguez, the predicted value is rounded to integer number for integer prediction-error expansion (IPE) embedding. The rounding operation makes a constraint on a predictor’s performance. In this paper, we propose a non-integer PE (NIPE) embedding approach, which can proceed non-integer prediction errors for embedding data into an audio or image file by only expanding integer element of a prediction error while keeping its fractional element unchanged. The advantage of the NIPE embedding technique is that the NIPE technique can really bring a predictor into full play by estimating a sample/pixel in a noncausal way in a single pass since there is no rounding operation. A new noncausal image prediction method to estimate a pixel with four immediate pixels in a single pass is included in the proposed scheme. The proposed noncausal image predictor can provide better performance than Sachnev et al.’s noncausal double-set prediction method (where data prediction in two passes brings a distortion problem due to the fact that half of the pixels were predicted with the watermarked pixels). In comparison with existing several state-of-the-art works, experimental results have shown that the NIPE technique with the new noncausal prediction strategy can reduce the embedding distortion for the same embedding payload.

On adaptive distortion control in reversible watermarking using modified reversible contrast mapping

The use of reversible contrast mapping (RCM) on pair of pixels in images for reversible watermarking (RW) offers a flexible integer transform that leads to high embedding rate at low mathematical complexity and without requiring additional data compression. Mathematically RCM may be interpreted as a form of adaptive linear transformation on pair of pixels that controls distortion and leads to the retention of the structural information of the watermarked image. A generalized form of RCM, analogous to M-ary modulation in communication, is developed here for a set of points and their corresponding embedding spaces are shown geometrically with and without considering distortion control. Then an optimized distortion control framework adaptive to the choice of operating points is considered to improve data hiding capacity under embedding distortion constraint. Simulation results show that the combination of different M-ary approaches i.e. using the points representing the different transformation functions outperform the embedding rate, visual quality and security of the hidden information compared to the existing RCM, difference expansion (DE) and prediction error expansion (PEE) methods during over embedding. Numerical results show that an average of 13 % improvement in visual quality, 25 % improvement in security of the hidden data is achieved at 0.8 bpp embedding rate over existing PEE work. Performance in robustness against common signal processing operations, namely noise addition, smoothing filtering and some form of geometric operation like random bending attack are also studied. All these studies and the effectiveness are demonstrated with a large set of simulation results.

High capacity reversible image watermarking using error expansion and context‐dependent embedding

A novel prediction error expansion-based reversible image watermarking technique is proposed. The proposed approach exploits the variance of neighbouring pixels in a way that the embedding of information bits in each pixel of an image depends on the scale of variation present in its eight neighbouring pixel values. A rhombus predictor is employed in the proposed scheme for its efficacy. In contrast to existing prediction-error expansion based reversible watermarking techniques, the embedding phase of the proposed approach depends only on the context of the pixel to be watermarked. Exploiting the neighbourhood information of each pixel in this way, avoids the need of a location map (LM) or histogram shifting (HS) to distinguish marked pixels from unmarked pixels. Consequently, the proposed approach does not require a large amount of auxiliary information in the form of a LM or HS of unmarked pixels, and thus introduces less distortion, yielding good embedding performance, especially at high capacity.

Region based interpolation error expansion algorithm for reversible image watermarking

In this paper, we propose a new reversible image watermarking method based on interpolation-error expansion called region based interpolation error expansion (RBIEE). We improved Thodi's prediction error expansion (PEE) technique by using a novel interpolation algorithm which exploits interpixel correlation better. Furthermore, interpolation error histogram is divided into two regions. The parameters of each region are determined separately and iteratively to reach a given embedding capacity more precisely. However, adaptive embedding strategy is utilized to get better capacity-distortion performance. Advantage of the proposed method over the other state-of-the-art methods in terms of capacity and visual quality is demonstrated experimentally.

Minimum Rate Prediction and Optimized Histograms Modification for Reversible Data Hiding

Prediction-error expansion (PEE)-based reversible data hiding schemes consist of two steps. First, a sharp prediction-error (PE) histogram is generated by utilizing pixel prediction strategies. Second, secret messages are reversibly embedded into the prediction-errors through expanding and shifting the PE histogram. Previous PEE methods treat the two steps independently while they either focus on pixel prediction to obtain a sharp PE histogram, or aim at histogram modification to enhance the embedding performance for a given PE histogram. This paper propose a pixel prediction method based on the minimum rate criterion for reversible data hiding, which establishes the consistency between the two steps in essence. And correspondingly, a novel optimized histograms modification scheme is presented to approximate the optimal embedding performance on the generated PE sequence. Experiments demonstrate that the proposed method outperforms the previous state-of-art counterparts significantly in terms of both the prediction accuracy and the final embedding performance.