Reversible Data Hiding Methods Research Articles

Reversible data hiding in halftone images based on minimizing the visual distortion of pixels flipping

With the rapid development of multimedia technology, reversible data hiding (RDH) has been proposed as a technique to protect the hidden information and recover the original image. However, the visual quality of the existing RDH methods in halftone images is not typically satisfactory. In this paper, to reduce the visual distortion, a novel RDH method in halftone images based on minimizing the visual distortion of pixels flipping is proposed. First, based on the human visual system, the suitable marked blocks are selected by the designed visual quality score. Then, by analyzing the block structure of halftone images, the optimal flippable pixels in the suitable marked blocks are selected to embed data. One of the advantages of the proposed method is that the distortion caused by pixels flipping is minimal. The other is that to achieve reversibility, the positions of these optimal flippable pixels can be directly determined by calculating the minimal visual distortion without recording the location map. Experimental results demonstrate that the proposed method ensures high visual quality for marked images and achieves high embedding payload.

Reversible Data Hiding Based on Structural Similarity Block Selection

Reversible data hiding (RDH) methods are widely used in many privacy-sensitive real-time applications for digital images. As an efficient RDH method, prediction-error histogram (PEH) shifting technique has found wide application for its high efficiency in increasing embedding capacity. Nowadays, the performance of most PEH-based methods is evaluated only by Peak Signal to Noise Ratio (PSNR) or Structural Similarity (SSIM) Index. However, more and more applications require high quality in both PSNR and SSIM. Therefore, in this paper, we propose a new RDH method to concentrate on both PSNR and SSIM performance, which involves two key techniques: 1) an SSIM-based block selection technique, 2) a PEH-based optimal expansion bins selection technique. Through block selection, the original image is divided into two parts: smooth blocks and rough blocks. We select pixels of smooth block for double embedding and leave pixels of rough block unchanged. According to the optimal expansion bins, reasonable pixels are chosen for embedding such that the embedding distortion can be reduced. With these improvements, our proposed method has higher SSIM and PSNR after embedding compared to other PEH-based methods. The experimental results demonstrate its superiority over some state-of-the-art counterpart and other conventional PEH-based works.

New High Capacity Reversible Data Hiding Using the Second-Order Difference Shifting

Reversible data hiding (RDH) algorithm is developing rapidly for many years. Usually, the histogram shifting (HS) based RDH algorithm has two main steps: Firstly, a steep difference histogram is generated with some effective methods for difference. Secondly, by extending and shifting some differences in the histogram, bits of information can be embedded into the cover image which can be restored reversibly. In this paper, we proposed the second-order difference to obtain a steeper difference histogram. Firstly, we slide a window with size 2 × 2 through the image. For each block, we can get the two first-order differences by calculating the absolute values of the two differences for its two columns. Thus, the second-order difference of each block, which is the absolute values of the difference of the two first-order differences, can be obtained. By extending and shifting the second-order difference, a bit may be embedded into the block finally. Experiments reveal that the proposed algorithm outperforms the previous state-of-the-art RDH methods in terms of the computational complexity, image distortion and the embedding performance.

Location-Based PVO and Adaptive Pairwise Modification for Efficient Reversible Data Hiding

Pixel-value-ordering (PVO) is an efficient technique of reversible data hiding (RDH). By PVO, the maximum and minimum in each cover image block are first predicted and then modified to embed data. Actually, many PVO-based methods are essentially based on high-dimensional histogram modification. For these methods, a two-dimensional (2D) prediction-error histogram (PEH) is first generated and then modified based on a 2D mapping. However, these methods have two drawbacks. On one hand, the generated 2D PEH is irregular so that it is difficult to design suitable histogram modification strategy. On the other hand, the employed 2D mapping is empirically designed, and thus the embedding performance is far from optimal. Based on these considerations, a new PVO-based RDH scheme is proposed in this paper. By considering both pixel value orders and pixel locations, a new predictor is proposed so that the generated 2D PEH is regular in shape and suitable for reversible embedding. Moreover, instead of manually designing 2D mappings, to optimize the embedding performance, a self-learning mechanism is proposed to adaptively select the 2D mapping according to the image content. With the new predictor and the self-learning mechanism for 2D mapping selection, the proposed method works well with a good marked image quality, e.g., the PSNR of the image Lena is as high as 61.53 dB for an embedding capacity of 10 000 bits. Besides, compared with some state-of-the-art RDH methods, the superiority of the proposed method is experimentally verified.

A Secure and Privacy-Preserving Technique Based on Contrast-Enhancement Reversible Data Hiding and Plaintext Encryption for Medical Images

Protection of medical data has become a prerequisite in medical imaging clouds due to the semi-trusted cloud. Aiming at preserving patients’ privacy and increasing the security of medical images in the cloud, this letter proposes a secure and privacy-preserving technique which provides a new security mechanism for medical data. In this technique, a novel reversible data hiding (RDH) based on adaptive texture classification is proposed to embed privacy data into medical images for preserving patients’ privacy and improving image quality, and plaintext encryption is proposed to encrypt the marked medical image into the similar image of target image for increasing image security. Extensive experiments have shown that the proposed RDH is better than other RDH methods. Plaintext encryption can reduce the attacker's attention and increase the security of medical images well.

A Novel Adjustable RDH Method for AMBTC-Compressed Codes Using One-to-Many Map

Most existing AMBTC (absolute moment block truncation coding)-based reversible data hiding (RDH) schemes cannot be decoded by traditional AMBTC decoders, because they change the coding structure of the AMBTC compressed code stream, which is unidentified for decoders. Even if some AMBTC-based RDH methods can be decoded by traditional AMBTC decoders, but the obtained payload is low. To this end, in this paper, a high-capacity and decodable AMBTC-based RDH scheme is presented. It is well observed that an $m\times n$ -sized bitmap has $2^{m\times n}$ combinations of ‘1’ and ‘0’. However, all the bitmaps of an image occupy only a small part of all the combinations, and a large part are not used in this image. Motivated by this observation, we propose a one-to-many map between the used and unused combinations, in which one used bitmap can be mapped to multiple unused combinations, achieving high payloads (e.g., $\log _{2} 25$ bits for one bitmap of Lena), and more importantly, completely reserving the coding structure of the AMBTC-compressed code. Unlike existing decodable AMBTC-based RDH methods only capable of achieving a fixed payload for one test image, our method can adjust adaptively payloads according to the required visual quality by introducing a predefined threshold. The experimental results also demonstrate that our proposed scheme can largely increase the payload on the basis of maintaining good visual quality.

A 3-layer RDH method in encrypted domain for medical information security

Digitisation of sensitive images demands a lossless security mechanism and a sophisticated privacy preservation technique. Sensitive imagery, e.g., medical, forensic, military images, etc., needs special care during transmission as a little distortion can lead to catastrophic diagnosis mistake. With immense advancements, popularity, and success of service-oriented architecture (SOA), providing safe and secure online medical facility is one hard challenge for both research community and the industry. This paper proposes a 3-layer embedding mechanism enabled reversible data hiding (RDH) scheme with additional electronic patient record (EPR) hiding technique for encrypted medical images. LSB modification and LSB substitution technique are used for the embedding and EPR hiding. The experiments carried out on the medical test images on three levels of embedding and the experimental results show great potential in terms of security, embedding capacity, and recovered image quality.

High-fidelity dual-image reversible data hiding via prediction-error shift

Dual-image reversible data hiding (RDH) technology has been an active research area and became an essential part of information security because of its unique advantages in security, embedding capacity and visual quality. For a dual-image RDH strategy, message data is embedded into a cover image to generate two marked images with similar visual quality. In this study, we propose an improved dual-image RDH algorithm based on a prediction-error shift strategy. As a more compact image feature, prediction error is exploited to make better use of the redundancy of image content with the maximum distortion of one. A bidirectional-shift strategy is used to extend the shiftable positions in the central zone of the allowable coordinates. Next, we design a new shiftable-position model for the prediction error corresponding to the peak value in the prediction-error histogram to embed more message bits without causing more distortion. The experimental results demonstrate the efficacy and high-fidelity of the proposed method, especially in relatively low embedding rate circumstances. The average peak signal-to-noise ratio gain of the proposed method on eight standard test images is at least 1.15 dB for the given embedding capacity of 50,000 bits compared with other state-of-the-art dual-image RDH methods.

A 3-layer RDH method in encrypted domain for medical information security

Digitisation of sensitive images demands a lossless security mechanism and a sophisticated privacy preservation technique. Sensitive imagery, e.g., medical, forensic, military images, etc., needs special care during transmission as a little distortion can lead to catastrophic diagnosis mistake. With immense advancements, popularity, and success of service-oriented architecture (SOA), providing safe and secure online medical facility is one hard challenge for both research community and the industry. This paper proposes a 3-layer embedding mechanism enabled reversible data hiding (RDH) scheme with additional electronic patient record (EPR) hiding technique for encrypted medical images. LSB modification and LSB substitution technique are used for the embedding and EPR hiding. The experiments carried out on the medical test images on three levels of embedding and the experimental results show great potential in terms of security, embedding capacity, and recovered image quality.

Reversible Data Hiding by Using Adaptive Pixel Value Prediction and Adaptive Embedding Bin Selection

In this letter, a reversible data hiding (RDH) scheme by using adaptive pixel value prediction and adaptive embedding bin selection based on pixel-based pixel value ordering (PPVO) is proposed. Different from the previous PPVO based methods, each to-be-embedded pixel is predicted by its neighbor pixels, which are selected adaptively according to the complexity of its neighbor pixel values. Moreover, the value range of embedding bin is not constrained in our method. Especially, when the value of embedding bin is less than 0, its value is updated adaptively during the process of embedding and extracting secret bits. Our maximum embedding capacity (EC) is improved significantly due to unconstrained embedding bin. In addition, the optimal embedding bins can be selected to achieve highest visual quality under a given EC. Experimental results show that the proposed method outperforms some state-of-the-art PPVO based RDH methods.

Improved PPVO-based high-fidelity reversible data hiding

Most pixel-value-ordering (PVO) based reversible data hiding (RDH) methods conduct pixel prediction on image blocks, while a recently proposed pixel-based PVO (PPVO) method changes the blockwise prediction in PVO to pixelwise manner and achieves better embedding performance. However, in PPVO, the prediction is not accurate since some pixels close to the to-be-predicted one are not utilized. Moreover, pixels with different local complexity are not fully exploited and the embedding performance of PPVO is not optimized. Thus, to better determine the prediction context as well as full use the image local correlation, an improved PPVO-based RDH method is proposed in this paper. First, to improve the prediction accuracy, a new predictor is proposed in which the prediction context is properly selected. Then, for the optimized performance, a new embedding strategy is proposed based on multiple histograms generation and modification with multi-sized prediction contexts. Experimental results verify that the proposed method is superior to PPVO and some other state-of-the-art RDH methods.

Reversible data hiding in JPEG images based on zero coefficients and distortion cost function

Recently, reversible data hiding (RDH) in joint photographic experts group (JPEG) images has received a great deal of attention since the JPEG image is one of the most popularly used image formats nowadays. Generally in JPEG image, the quantized discrete cosine transform (DCT) coefficients with the value of zero are far more than those with nonzero coefficients. However, most existing methods are avoided to change these zero coefficients for fear of JPEG file size increase, and the designed distortion cost functions are not perfect. In this paper, we propose a new JPEG RDH method in which the numerous zero coefficients can be modified. Based on an improved distortion cost function, a strategy which can measure the distortion for each coefficient and select coefficient positions which are most suitable for embedding is proposed. With this strategy, zero coefficients are applied to data embedding and the embedding performance is improved. Experimental results have proved that the proposed method can effectively reduce the embedding distortion and increase the embedding capacity while maintaining a relative good file size.

Error-free separable reversible data hiding in encrypted images using linear regression and prediction error map

In this paper, we propose a new reversible data-hiding method in encrypted images. The method is a vacating room after encryption (VRAE) method that attempts to achieve the error-free recovery of images. In previous VRAE methods, errors may occur in recovering images because of inaccurate predictions, so these methods cannot achieve complete reversibility. In addition, these methods have limited embedding rates. To solve these problems, the proposed method uses a linear, regression-based predictor to improve the accuracy of predictions, and it uses a prediction error map to eliminate errors caused by inaccurate predictions. By using the linear regression-based predictor and the prediction error map, the embedding rate of data embedding is improved significantly, and the original image can be recovered with no error. The experimental results showed that the embedding rate of the proposed method can be higher than 0.5 bpp, and the visual quality can be maintained at high embedding rates.

High Capacity Reversible Data Hiding Based on Multiple Histograms Modification

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

Reversible Data Hiding Using Inter-Component Prediction in Multiview Video Plus Depth

With the advent of 3D video compression and Internet technology, 3D videos have been deployed worldwide. Data hiding is a part of watermarking technologies and has many capabilities. In this paper, we use 3D video as a cover medium for secret communication using a reversible data hiding (RDH) technology. RDH is advantageous, because the cover image can be completely recovered after extraction of the hidden data. Recently, Chung et al. introduced RDH for depth map using prediction-error expansion (PEE) and rhombus prediction for marking of 3D videos. The performance of Chung et al.’s method is efficient, but they did not find the way for developing pixel resources to maximize data capacity. In this paper, we will improve the performance of embedding capacity using PEE, inter-component prediction, and allowable pixel ranges. Inter-component prediction utilizes a strong correlation between the texture image and the depth map in MVD. Moreover, our proposed scheme provides an ability to control the quality of depth map by a simple formula. Experimental results demonstrate that the proposed method is more efficient than the existing RDH methods in terms of capacity.

Improved reversible data hiding based on PVO and adaptive pairwise embedding

Pixel-value-ordering (PVO) is an efficient technique of reversible data hiding (RDH). By PVO, the cover image is first divided into non-overlapping blocks with equal size. Then, the pixel values in each block are sorted in ascending order. Next, take the second largest/samllest pixel value as a prediction of the largest/samllest pixel value to derive two prediction errors. Finally, the data embedding is constructed by modifying the generated prediction errors of each block. After data embedding, the PVO of each block is unchanged, which guarantees the reversibility. Our key observation is that, in each block, the modification for the two prediction errors is independent without exploiting the correlation between them, although they are closely correlated with each other. In light of this, an improved PVO-based RDH method is proposed in this work. The two prediction errors of each block are considered as a pair, and the pairs are modified for data embedding based on adaptive two-dimensional histogram modification. The proposed method is experimentally verified better than the original PVO-based method and some of its improvements.

Reversible data hiding in compressed and encrypted images by using Kd-tree

In this paper, a joint scheme and a separable scheme for reversible data hiding (RDH) in compressed and encrypted images by reserving room through Kd-tree were proposed. Firstly, the plain cover image was losslessly compressed and encrypted with lifting based integer wavelet transform (IWT) and set partition in hierarchical tree (SPIHT) encoding. Then, several shift operations were performed on the generated SPIHT bit-stream. The shifted bit-stream was restructured into small chunks and packed in the form of a large square matrix. The binary square matrix was exposed to Kd-tree with random permutations and reserving uniform areas of ones and zeros for secret data hiding. After that, a joint or a separable RDH scheme can be performed in these reserved spaces. In the joint RDH scheme, the secret data were embedded in the reserved spaces before encrypting with multiple chaotic maps. Thus, secret data extraction and cover image recovery were achieved together. In the separable RDH scheme, the secret data were embedded in the reserved spaces after encrypting with multiple chaotic maps. Since message extraction and cover image recovery are performed separately, anyone who has the embedding key can extract the secret message from the marked encrypted copy, while cannot recover the cover image. A complete encoding and decoding procedure of RDH for compressed and encrypted images was elaborated. The imperceptibility analysis showed that the proposed methods bring no distortion to the cover image because there was no change to the original cover image. The experimental results showed that the proposed schemes can perform better for secret data extraction and can restore the original image with 100% reversibility with much more embedding capacity and security. The proposed schemes significantly outperform the state-of-the-art RDH methods in the literature on compressed and encrypted images.

A Secure and High Visual-Quality Framework for Medical Images by Contrast-Enhancement Reversible Data Hiding and Homomorphic Encryption

Medical data security is facing great challenges in medical applications due to the open internet and the semi-trusted cloud. For the sake of privacy protection and the security of medical images, this paper proposes a secure and high visual-quality framework for medical images. In this framework, a novel reversible data hiding (RDH) based on lesion extraction embeds privacy data into medical images for privacy protection and image quality improvement, homomorphic encryption based on chaotic map encrypts images for medical image security. The experiments have shown that the proposed framework increases the security of medical data and improves the visual quality of medical images significantly. The proposed RDH in this framework outperforms the other RDH methods with contrast enhancement and the proposed encryption scheme increases image security well.

Reversible Data Hiding Scheme Based on Quad-Tree and Pixel Value Ordering

This study proposes a reversible data hiding (RDH) scheme based on pixel value ordering (PVO). RDH schemes based on PVO technique generally realize the prediction procedure by employing the sorted pixels in a block-wise manner, and then embedding secret data by expanding the prediction errors. Since these operations are carried out based on the block, and pixel intensity has spatial correlation such that the neighboring pixels have similar values, the block patterns and sizes certainly affect the embedding performance of the PVO-based RDH method. The original PVO-based RDH schemes use fixed block size. However, due to the differences in image complexity, fixed-size blocks have many limitations. Thus far, several methods that break the limitations have been proposed, such as setting a threshold value, and determine the size from the complexity of the block, using sliding window instead of fixed blocks. These methods significantly enhanced the embedding performance of PVO-based RDH schemes, but this can still be improved. The proposed scheme introduces the quad-tree structure, which combines the advantages of several previously proposed methods to design a flexible block patterns, thus fully utilizing the characteristics of the image itself in embedding secret data. Experimental results indicate that the proposed scheme is flexible and better than previous approaches.

Improved K-Pass Pixel Value Ordering Based Data Hiding

K-pass pixel value ordering (PVO) is an effective reversible data hiding (RDH) technique. In k-pass PVO, the complexity measurement may lead to a weak estimation result because the unaltered pixels in a block are excluded to estimate block complexity. In addition, the prediction-error is computed without considering the location relationship of the second largest and largest pixels or the second smallest and smallest pixels. To this end, an improved RDH technique is proposed in this paper to enhance the embedding performance. The improvement mainly lies in the following two aspects. First, some pixels in a block, which are excluded from data hiding in some existing RDH methods, are exploited together with the neighborhood surrounding this block to increase the estimation accuracy of local complexity. Second, the remaining pixels in a block, i.e., three largest and three smallest pixels are involved in data embedding. Taking three largest pixels for example, when the difference between the largest and third largest pixels is relatively large (e.g., > 1), we improve k-pass PVO by considering the location relationship of the second largest and largest pixels. The advantage of doing this is that the difference valued 3 between the maximum and the second largest pixel which is shifted in k-pass PVO, is able to carry 1 bit data in our method. In other words, a larger amount of pixels are able to carry data bits in our scheme compared with k-pass PVO. Abundant experimental results reveal that the proposed method achieves preferable embedding performance compared with the previous work, especially when a larger payload is required.