Reversible Data Hiding Methods Research Articles

Real-time reversible data hiding based on multiple histogram modification

Prediction-error expansion (PEE) has been widely utilized in reversible data hiding (RDH) due to its advantage of high-quality marked image. Recently, a RDH method based on multiple histogram modification (MHM) has been proposed, achieving good performance at low capacity. However, its exhaustive expansion-bin-selection mechanism is time consuming, so that it can only choose a single pair of expansion bins in each histogram for embedding data. As an extension of MHM, a novel RDH scheme for high-capacity embedding is proposed in this paper. In the proposed method, multiple pairs of expansion bins are utilized in each histogram and a greedy search algorithm is designed to determine the nearly optimal expansion bins. The results of experiments demonstrate that the proposed scheme has real-time performance and high-quality marked image, and it outperforms the original MHM-based RDH method and some other state-of-the-art works.

Reversibility improved data hiding in 3D mesh models using prediction-error expansion and sorting

Reversible data hiding (RDH) recover the original media without any distortion after extraction of the hidden data. Since massive three-dimensional (3D) mesh models have been constructed from high-dimensional image data nowadays, existing deficiencies of prior studies on RDH of 3D mesh models need to be overcome for performance improvement. In this paper, we propose an improved RDH method for 3D mesh models based on prediction-error expansion and sorting. Instead of considering only several adjacent neighbors, the proposed method predict a vertex position with a ring pattern for better prediction accuracy. To make better capacity-distortion control, data bits are reversibly embedded into 3D mesh models with embedding operations including expansion, shifting and LSB replacement. Moreover, we propose smoothness sorting and twice-layered strategy to enhance the embedding performance further. Thus, the proposed method achieves improved embedding performance in terms of capacity enhancement and distortion reduction. Experimental results demonstrate that the proposed method produces relatively superior results compared with previous related state-of-the-art works.

Reversible data hiding for high dynamic range images using edge information

In this work, a reversible data hiding (RDH) algorithm is proposed for high dynamic range (HDR) images containing an additional luminance channel. Since prediction accuracy is the key of RDH, we propose to embed data into HDR images by exploiting the edge information among the luminance channel and color channels to achieve accurate prediction and high embedding capacity. Besides, a new edge-directed order is presented to reduce the visual loss of the stego image. Various experimental results demonstrate that the proposed algorithm can hide more data into HDR images with less distortion than directly applying traditional RDH methods designed for low dynamic range (LDR) images. Compared to the current HDR hiding algorithms, the proposed method is not only reversible, but also achieves a tradeoff between embedding capacity and distortion.

3D PEE mapping based reversible data hiding for color images

The performance of prediction-error expansion (PEE) for reversible data hiding (RDH) is excellent. 2D and 3D PEE has a great improvement than the conventional PEE. However, the PEE-based method used in each channel of a color image cannot make full use of the correlation among three channels. In the paper, a reversible data hiding algorithm based on 3D PEE mapping is proposed. Firstly, a prediction-error triple is generated from three channels of a color image. Next, the prediction error triples can shift freely in 3D space. Finally, specific error triples have been shifted to embed secret data and other triples are expanded to make room for data embedding. By using the proposed reversible 3D PEE mapping, the inter-correlation of RGB channels is better exploited. Extensive experimental results demonstrate that the proposed algorithm outperforms the traditional RDH methods for color images.

An improved reversible data hiding scheme using best neighboring coding based on color images

In this paper, an improved reversible data hiding (RDH) scheme, best neighboring coding (BNC), is proposed for vector quantization (VQ) compressed color images. As a result, it improves joint neighboring coding (JNC) based schemes, which take advantage of correlation between indices to embed secret bits (SB) and achieves good compression rate. As the correlation between RGB channels and the information of reference channel are employed, the best neighboring blocks of the other channels are obtained. The experimental results indicate that BNC enhances the prediction possibility of the best neighboring index by about 2.5 times compared to the conventional JNC method, which applies the current index and one of its four neighbor indices pointed by the secret data to generate the difference. Consequently, a more concentrated distribution of difference between current index and its neighboring index is obtained. After encoding this distribution generated by RGB channels, better compression rate and higher embedding efficiency are derived. Furthermore, extended experiments demonstrate that the proposed method outperforms other state-of-the-art RDH methods as well.

A high capacity reversible data hiding scheme based on right-left shift

Abstract Recently, data hiding scheme developed rapidly. But its permanent distortion does not satisfy some sensitive applications. Accordingly, reversible data hiding (RDH) has developed quickly for years. Histogram shifting (HS) technique is one of the high-efficiency RDH schemes. In this paper, based on the characteristics of rectangle-prediction-error distribution, i.e., bin 0 is the highest bin, the other peak bins are distributed on the left and right sides of the highest bin in a nearly symmetrical way, and many zero bins locate on both sides of the peak bins. We proposed a high capacity RDH scheme based on right-left shift. The peak bins are first shifted towards right direction to leave some locations for embedding secret message bits, then, they are shifted towards left direction leave more locations for more data embedding, which can reduce the distortion due to right shift. By analyzing the necessary condition for the predictive error, the issue of overflow/underflow can be easily solved without adding side information in the embedding data. Experimental results on different images illustrate the proposed scheme is of high embedding capacity (EC) and visual quality on different images, meanwhile, the theory demonstrates the performance of the proposed method outperforms other RDH methods.

Tailoring reversible data hiding for 3D synthetic images

In this paper, we proposed a reversible data hiding (RDH) method for three-dimensional (3D) synthetic images based on the depth-no-synthesis-error model (D-NOSE). By using the depth-image-based rendering technique, the 3D image can be synthesized from a 2D color image and its corresponding depth map to provide the viewer a depth perception. Recently, a 3D-based RDH method is proposed to modify the depth map while still preserving the perfect 3D image synthesization. However, it has not fully exploited the pixel mapping relations in the synthesization, resulting in an insufficient capacity. Moreover, the optimal 3D visual quality is not discussed. To compensate this, we propose to improve the capacity and the 3D visual quality. The proposed method extends the pixels expansion toward two directions to fully exploit the allowable range, and can adjust the pixel modification to control the capacity. Furthermore, for a given capacity, the optimal baseline can be selected for a better 3D visual quality. Experimental results show that the proposed method outperforms the previous methods in terms of capacity and distortion, and a better 3D visual quality can be obtained as well.

Sparse recovery based reversible data hiding method using the human visual system

In this paper, a novel sparse recovery based reversible data hiding (RDH) method using the human visual system (HVS) is presented. To improve the low accuracy of existing predictors, a sparse recovery based predictor is proposed. In the processes of sparse recovery, the most relevant neighbors can be adaptively chosen by using sparse representation to predict the current pixel accurately, and thus the concentrated prediction error histogram (PEH) is built to obtain good embedding performance. Moreover, to overcome the conflict between the embedding order of the traditional RDH method and the evaluation of HVS, a new embedding strategy based on just noticeable difference (JND) is designed. In this strategy, pixels are classified into sensitive and in-sensitive clusters according to JND values, and two corresponding PEHs are built. Accordingly, different inner regions of two PEHs are adjusted to meet the required embedding capacity, and the prediction error expansion (PEE) technique is utilized to embed data. Experimental results prove that the proposed method outperforms the state-of-the-art RDH methods, including JND related methods.

Temporal and spatial correlation-based reversible data hiding for RGB CFA videos

Considering a mosaic video, in which each mosaic image could be captured by any of the existing eleven RGB color filter array (CFA) structures and each mosaic pixel contains only one primary color, this paper proposes a novel spatial and temporal correlation-based reversible data hiding (RDH) method. In the proposed RDH method, an intellectual switching strategy to select the spatial correlation-based or the temporal prediction scheme to maximize the quality improvement of the marked mosaic videos under fixed embedding capacity. Based on eight test mosaic videos, the experimental results demonstrate that in terms of peak signal-to-noise ratio (PSNR), color PSNR (CPSNR), structure similarity (SSIM) index, motion-based video integrity evaluation (MOVIE) index, and human perceptual effect, the proposed RDH method clearly outperforms the spatial correlation-based RDH method by Yang et al. and the other four state-of-the-art RDH methods.

A ROI-based high capacity reversible data hiding scheme with contrast enhancement for medical images

In this paper, we attempt to investigate the secure archiving of medical images which are stored on semi-trusted cloud servers, and focus on addressing the complicated and challenging integrity control and privacy preservation issues. With the intention of protecting the medical images stored on a semi-trusted server, a novel ROI-based high capacity reversible data hiding (RDH) scheme with contrast enhancement is proposed in this paper. The proposed method aims at improving the quality of the medical images effectively and embedding high capacity data reversibly meanwhile. Therefore, the proposed method adopts “adaptive threshold detector” (ATD) segmentation algorithm to automatically separate the “region of interest” (ROI) and “region of non-interest” (NROI) at first, then enhances the contrast of the ROI region by stretching the grayscale and embeds the data into peak bins of the stretched histogram without extending the histogram bins. Lastly, the rest of the required large of data are embedded into NROI region regardless its quality. In addition, the proposed method records the edge location of the segmentation instead of recording the location of the overflow and underflow. The experiment shows that the proposed method can improve the quality of medical images obviously whatever in low embedding rate or high embedding rate when compared with other contrast-based RDH methods.

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.

RDH in BCS images based on block edge pixel separation

By block compressed sensing (BCS), the image can be compressed and encrypted simultaneously. A novel reversible data hiding (RDH) method is proposed to embed additional data into the BCS encrypted images. In the proposed method, based on the image block edge pixel separation, every block can be used for data embedding through a special measurement flipping. After decryption, considering the correlation of natural images, the fluctuation function can be used to find out the flipped block and realise the data extraction and image recovery. Experiments show that the proposed method can outperform the existing RDH method in BCS images.

Improved dual-image reversible data hiding method using the selection strategy of shiftable pixels' coordinates with minimum distortion

Compared to conventional reversible data hiding (RDH) methods, the dual-image RDH technique is an effective way to achieve greater embedding rate and better quality of the stego images. Motivated by the center folding-based, dual-image RDH method proposed by Lu et al. (SIGNAL PROCESSING, 115 (2015) 195–213), we propose an improved dual-image RDH method by using the selection strategy of shiftable pixels' coordinates with minimum distortion. Although the stego dual-pixel in the center folding-based method is shifted with minimum distortion, there is another shiftable pixel-coordinate that can be used without creating any extra distortion. Based on this observation, the embedding rate can be improved without any deterioration of visual quality by embedding an extra message bit into each newfound alternative pixel-coordinate. In addition, the range of parameter k, which controls the embedding rate, in our method is extended to greater than or equal to 1, therefore, a higher visual quality can be achieved when k is set at 1. The experimental results showed that the embedding rate of our method is greater than that of the center folding-based method for the same stego image quality, and our method outperforms other methods, whether in term of average PSNR or embedding rate.

Improved reversible data hiding based on two-dimensional difference-histogram modification

Recently, a reversible data hiding (RDH) method based on difference-pair-mapping (DPM) is proposed for better employing the image redundancy. The DPM is a specifically designed injective mapping defined on difference-pairs, and it is an extension of the conventional expansion and shifting techniques used in prior histogram-based RDH. However, any injective mapping defined on difference-pairs with allowed modification directions can derive a RDH method and the embedding performance is closely related to the employed mapping. Then, enhanced performance can be expected if a better DPM is adopted. Based on this consideration, a new RDH method is presented in this paper to improve the previous DPM-based RDH. Specifically, based on the statistical distribution of difference-pairs, a new DPM is designed to better utilize the difference-pairs with high frequencies. Moreover, a fine adjusting strategy is introduced to further optimize the embedding performance by adaptively adjusting the proposed DPM. With the proposed DPM and the adjusting strategy, our method outperforms the previous DPM-based RDH. The superiority of our method over some other state-of-the-art works is also experimentally verified.

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.

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.

Reversible data hiding with contrast enhancement using adaptive histogram shifting and pixel value ordering

Existing image-based reversible data hiding (RDH) methods tend to focus on increasing embedding capacity, but few consider keeping or improving visual image quality. Wu et al. proposed a new RDH method with contrast enhancement (RDH-CE) by pair-wisely expanding the histogram to the lower end and upper end. RDH-CE is especially valuable in exploiting the details of poorly illustrated images for which the visibility of image details is more important than just keeping PSNR high. However, obvious visual image distortion appears when embedding level gets high, and embedding capacity is relatively low when embedding level is small. In this paper, Wu et al.'s work is improved from three perspectives, namely image contrast enhancement, visual distortion reduction, and embedding capacity increment. The image contrast is improved by making the histogram shifting process adaptive to the histogram distribution characteristics, the image visual distortion is reduced by cutting off half the modification range of pixels induced in histogram pre-shifting, and the embedding capacity is increased by exploiting the pixel value ordering technique at the early stage of data embedment. Experimental result proves that the proposed work is effective in improving image contrast, reducing visual image distortion, and increasing embedding capacity.

Hiding Data into Reserve Space before Image Encryption using Blowfish Algorithm

Nowadays, with the fast development of information technology more images and data are available on the internet. Hence there is a need to have some kind of authentication to that datawith increase in technology. This paper is about encryption and decryption of images using a private-key block cipher called 64-bits. Blowfish designed to increase security and to improve performance. This algorithm will be used as a variable key size up to 448 bits. The reversible data hiding (RDH) in encrypted images, is most powerful technology than others. Reversible Data Hiding (RDH) maintains the tremendous property that is the original content can be losslessly recover after embedding data. Reversible Data hiding phenomena is defined as a scheme that allows complete and visionless restoration of the original host data. All existing method of embedded data by reversible vacating room from the encrypted images which may have some faults on the data extraction or image restorations. The reversible data hiding is also known as the new watermarking technique which is used to derive an image by embedding some data on it as a watermark. A novel method is proposed by reserving room for embedding data before encryption of an image takes place with the available RDH algorithm and methods. Now the authentic person can secrete the data definitely on the image to provide authentication. Reversible technique is in the sense extraction of the original input image.

Reversible data hiding by adaptive group modification on histogram of prediction errors

In this work, the conventional histogram shifting (HS) based reversible data hiding (RDH) methods are first analyzed and discussed. Then, a novel HS based RDH method is put forward by using the proposed Adaptive Group Modification (AGM) on the histogram of prediction errors. Specifically, in the proposed AGM method, multiple bins are vacated based on their magnitudes and frequencies of occurrences by employing an adaptive strategy. The design goals are to maximize hiding elements while minimizing shifting and modification elements to maintain image high quality by giving priority to the histogram bins utilized for hiding. Furthermore, instead of hiding only one bit at a time, the payload is decomposed into segments and each segment is hidden by modifying a triplet of prediction errors to suppress distortion. Experimental results show that the proposed AGM technique outperforms the current state-of-the-art HS based RDH methods. As a representative result, the proposed method achieves an improvement of 4.30dB in terms of PSNR when 105,000 bits are hidden into the test Lenna image.

Adaptive Reversible Data Hiding by Extending the Generalized Integer Transformation

This letter proposes a novel reversible data hiding (RDH) method with an adaptive embedding capability by extending the generalized integer transformation (GIT). We modify the GIT algorithm to a further generalized form, and accordingly we propose an adaptive embedding algorithm. When hiding data into the original image, parameters of the extended GIT can be identified according to the content of each block. Along with a multi-level location map, a better embedding efficiency of the net payload can be achieved. On the decoding side, the original image can be losslessly recovered after extracting the hidden message. Experimental results show that the proposed method outperforms state-of-the-art integer-transformation based RDH methods.