Privacy-preserving reversible data hiding based on quad-tree block encoding and integer wavelet transform

Applications on the cloud server have matured, and protecting the privacy of the content owner has attracted more attention. Privacy-Preserving Reversible data hiding (PP-RDH) is an efficient technique for embedding additional data into an encrypted image. In this paper, we propose a privacy-preserving reversible data hiding scheme using the quad-tree partition and Integer Wavelet Transform (IWT) techniques. Our scheme focuses on improving the embedding rate and quality of the recovered image when a 2 × 2-sized, block-based image encryption method is applied to ensure relative higher security. On this basis, the IWT technique transforms the encrypted image, and coefficients in three high frequency subbands are converted into 8-bit binary system. Then, the quad-tree partition technique encodes each 8 × 8-sized coefficient block, since there are many zeroes in the front bit planes. The experimental results indicated that our proposed scheme significantly improved the embedding rate, and guaranteed lossless image recovery and data extraction.     

Reversal of pixel rotation: A reversible data hiding system towards cybersecurity in encrypted images

Due to privacy and security concerns, the researches of reversible data hiding in encrypted images (RDHEI) have become increasingly important. Conventional schemes vacate the spare room after image encryption (VRAE) suffer from the low embedding rate, high error rate of data extraction, and imperfect image recovery. To address these issues, we propose a separable reversible data hiding scheme for encrypted images that utilizes a novel pixel rotation technique to embed data into fully encrypted images. The block complexities of four decrypted rotation states are considered when recovering image. To realize perfect image recovery, we further devise a lossless version (LPR-RDHEI). Experimental results demonstrate that the proposed PR-RDHEI scheme achieves an embedding rate of 0.4994 bpp on average and ensures lossless data extraction. Meanwhile, the proposed LPR-RDHEI scheme still has a 0.4494 bpp embedding rate on average. The embedding rates of our two schemes are significantly improved compared with state-of-the-arts.     

Adaptive reversible data hiding scheme based on integer transform

In this paper, we present a new reversible data hiding algorithm based on integer transform and adaptive embedding. According to the image block type determined by the pre-estimated distortion, the parameter in integer transform is adaptively selected in different blocks. This allows embedding more data bits into smooth blocks while avoiding large distortion generated by noisy ones, and thus enables very high capacity with good image quality. For instance, by the proposed method, we can embed as high as 2.17 bits per pixel into Lena image with a reasonable PSNR of 20.71 dB. Experimental results demonstrate that the proposed method outperforms some state-of-the-art algorithms, especially for high capacity case.     

A general framework for reversible data hiding in encrypted images by reserving room before encryption

In this paper a general framework to adopt different predictors for reversible data hiding in the encrypted image is presented. Employing linear regression, we propose innovative predictors that contribute more significantly to accomplish more payload than conventional ones. Reserving room before encryption (RRBE) is designated in the proposed scheme making possible to attain high embedding capacity. In RRBE procedure, pre-processing is allowed before image encryption. In our scheme, pre-processing comprises of three main steps: computing prediction-errors, blocking and labeling of the errors. By blocking, we obviate the need for lossless compression to when a content owner is not enthusiastic. Lossless compression is employed in recent state of the art schemes to improve payload. We surpass the prior arts exploiting proper predictors, more efficient labeling procedure and blocking of the prediction-errors.     

High-capacity reversible data hiding in encrypted image based on specific encryption process

This paper proposes a novel reversible data hiding method in encrypted images based on specific encryption process. In the proposed specific encryption algorithm, the stream cipher and prediction error are combined to vacate room for data embedding. After that, a permutation operation is performed on the encrypted image to improve the security. In the embedding process, we can embed a large amount of secret data in the encrypted image by pixel value expansion because most of the pixel values are less than 128 by the specific encryption process. At the receiver end, the encrypted image can be recovered from the marked encrypted image without knowing the secret data. Therefore, even if the recipient only has the encryption key, the original image will be perfectly recovered. If the recipient only has the data-hiding key, the secret data will be extracted. And if the recipient has both keys, the original image and the secret data are both available. The proposed method achieves a higher embedding capacity than that of methods based on vacating room after encryption. It does not require the image owner to perform reversible data hiding techniques on the original image, which is more convenient than methods based on reserving room before encryption. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods.     

SARDH: A novel sharpening-aware reversible data hiding algorithm

Reversible data hiding (RDH) algorithms allow data protection and exact recovery of the original cover image upon data extraction. Most of RDH algorithms attempt to embed data while maintaining high peak signal-to-noise ratio. However, it has been deemed recently that some applications may demand improving the image contrast while embedding data. Additionally, stego images with better contrast could be less attractive to attacker, given the fact they have no idea about the original cover image. In this paper, we propose a sharpening-aware RDH (SARDH) algorithm that is capable of embedding significant amount of data in addition to sharpening the image. Experimental results proved the ability of SARDH algorithm in embedding large payloads and providing sharper stego images.     

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.     

Encrypted signal-based reversible data hiding with public key cryptosystem

Encrypted image-based reversible data hiding (EIRDH) is a well-known method allowing that (1) the image provider gives the data hider an encrypted image, (2) the data hider embeds the secret message into it to generate the encrypted image with the embedded secret message to the receiver, and (3) finally the receiver can extract the message and recover the original image without encryption. In the literature, the data hider and image provider must be specific parties who know the shared key with the receiver in traditional encrypted image-based reversible data hiding. In this paper, we propose an encrypted signal-based reversible data hiding (ESRDH) with public key cryptosystem, not only for images. The proposed scheme is secure based on Paillier homomorphic encryption. Finally, the experimental results show that the proposed scheme has much payload and high signal quality.     

Separable reversible data hiding in encrypted image based on pixel value ordering and additive homomorphism

This work proposes a separable reversible data hiding scheme in encrypted images based on pixel value ordering (PVO). After the original image is encrypted using homomorphism encryption by the content owner, the data hider embeds the secret data in encrypted domain. The PVO strategy realizes hiding data in each block. Additive homomorphism guarantees the performance of PVO in encrypted domain is close to that in plain domain. Besides, the homomorphism encryption does not cause data expansion, and the payload can be further improved. With the watermarked encrypted image, if the receiver has only the data hiding key, he can extract the additional data. If the receiver has only the encryption key, he can obtain a decrypted image similar to the original one. If the receiver has both the data hiding key and the encryption key, he can extract the additional data without any error and recover the original image losslessly.     

Hybrid prediction-based pixel-value-ordering method for reversible data hiding

Pixel-value-ordering (PVO) is an effective and promising method of reversible data hiding (RDH) and has received much attention in recent years. To improve performance, a pixel-based PVO (PPVO) method was recently introduced to predict the pixels to be embedded in a pixel-wise manner instead of the block-wise manner used by PVO. However, for PPVO, the surrounding neighbors of the predicted pixels are underutilized; moreover, its embedding does not adapt to the local complexity of the image to be embedded. To overcome the shortcomings of PPVO, this paper proposes a novel PVO method based on hybrid prediction for RDH. First, the surrounding neighbors of the pixel to be predicted are fully utilized by a hybrid prediction method, which combines rhombus prediction and pixel-wise prediction. Second, a modified embedding scheme based on multiple histograms is presented for adaptive embedding. Experimental results show the superior performance of the proposed method by comparing it with state-of-the-art RDH methods.     

Improved joint reversible data hiding in encrypted images

This paper proposes an improved method of reversible data hiding in encrypted images (RDH-EI). Three parties constitute the proposed system: the image owner, the remote server and the recipient. To preserve privacy, an image owner encrypts the original image using a stream cipher algorithm and uploads the ciphertext to a remote server. On server side, a data-hider is allowed to embed additional message into the encrypted image using a swapping/shifting based algorithm. After downloading the marked encrypted image from the server and implementing the decryption, a recipient can extract the hidden messages and losslessly recover the original image. Experimental results show that the proposed method achieves a larger payload than the related works. Meanwhile, a limitation in the related works that few bits can be embedded into the encrypted medical images is also eliminated in the proposed method.     

An efficient high-capacity reversible data hiding scheme for encrypted images

Reversible data hiding in encrypted images is an effective technique to embed information in encrypted domain, without knowing the original content of the image or the encryption key. In this paper, a high-capacity reversible data hiding scheme for encrypted images based on MSB (most significant bit) prediction is proposed. Since the prediction is not always accurate, it is necessary to identify the prediction error and store this information in the location map. The stream cipher is then used to encrypt the original image directly. During the data hiding phase, up to three MSBs of each available pixel in the encrypted image are substituted by the bits of the secret message. At the receiving end, the embedded data can be extracted without any errors and the original image can be perfectly reconstructed by utilizing MSB prediction. Experimental results show that the scheme can achieve higher embedding capacity than most related methods.     

High-capacity reversible data hiding in encrypted images based on adaptive block encoding

This paper proposes a new high-capacity reversible data hiding scheme in encrypted images. The content owner first divides the cover image into blocks. Then, the block permutation and the bitwise stream cipher processes are applied to encrypt the image. Upon receiving the encrypted image, the data hider analyzes the image blocks and adaptively decides an optimal block-type labeling strategy. Based on the adaptive block encoding, the image is compressed to vacate the spare room, and the secret data are encrypted and embedded into the spare space. According to the granted authority, the receiver can restore the cover image, extract the secret data, or do both. Experimental results show that the embedding capacity of the proposed scheme outperforms state-of-the-art schemes. In addition, security level and robustness of the proposed scheme are also investigated.     

新颖的差值扩展可逆数据隐藏算法

提出一种变换方向自适应的差值扩展可逆数据隐藏算法(ACD),ACD通过扩展像素对的差值嵌入数据。像素对中一为参照像素,另一为变换像素,ACD根据像素对所处区域类型的估测,自适应选择变换像素,暗区选大值像素,亮区选小值像素,否则选默认方向像素。理论分析及实验结果表明,ACD解决了预测误差扩展可逆数据隐藏算法(PEE)在暗或亮载体时灰度溢出急剧增加的问题,与采用现有灰度溢出解决方案的PEE相比,改善了载体普适性,性能更稳定。     

Boosting up the data hiding rate through multi cycle embedment process

Prediction error based multi-layer data embedment schemes conceal secrets into several high frequency errors by modifying their prediction error histogram (PEH). It is investigated that k-times data embedment into n/k errors of PEH produces higher embedding payload, while maintaining better stego-image quality compared to those for embedding into n distinct errors for a single time only. This paper proposes a novel multi-cycle embedment scheme in which data is embedded into the errors of a defined range in each of its k cycles. Experiments were conducted to examine the performance of the proposed scheme comparing the multi-layer vs. multi-cycle embedding schemes individually and jointly. The scheme explores the points at which significantly better payloads can be obtained at the lower image distortions. Substantial improved performance were obtained during investigations, especially while large volume data embedment. The proposed scheme can embed massive and hybrid data of type text, numeric, image and audio.     

Reversible data hiding in encrypted images with separability and high embedding capacity

In this paper, a high-capacity reversible data hiding (RDH) scheme for encrypted images with separability is proposed. The image is first divided into non-overlapping blocks, and each block is encrypted with the same random value. The advantage is that the correlation between adjacent pixels can be preserved. Utilizing the preserved correlation, the prediction difference in encrypted domain is exactly the same as that of plaintext domain, so that the separability can be achieved. Without accessing the original image content, the data-hider can embed additional data into encrypted image through histogram shifting and difference expansion. At the receiving end, the embedded additional data and the original image can be recovered without any error in separable manner. Experimental results are presented to demonstrate the feasibility and efficiency of the proposed scheme.     

Adaptive multi-histogram reversible data hiding with contrast enhancement

Unlike existing reversible data hiding with contrast enhancement (RDHCE) methods, which excessively improve the image contrast for achieving the required capacity, the proposed method improves the image contrast appropriately while providing satisfactory embedding capacity. To this end, an adaptive multi-histogram RDHCE method is proposed in this study to improve the local and global contrast by considering the local properties of the histograms. On the one hand, fuzzy C-means clustering combining multiple features that are deliberately designed for contrast enhancement is employed to generate seven sharply-distributed prediction error histograms (PEHs). Subsequently, the genetic algorithm is utilized to adaptively select the optimal pairs achieving the best embedding performance for each PEH according to the local characteristics of PEH distribution, resulting in improving the local contrast adaptively and embedding significant amount of data. Additionally, two-sided histogram shifting (HS) is utilized to improve the global contrast appropriately while embedding reasonable amount of data. The experimental results demonstrate that the proposed method achieves better local and global contrast while providing a high embedding capacity compared with other existing RDHCE methods.     

Improved PVO-based reversible data hiding: A new implementation based on multiple histograms modification

Pixel-value-ordering (PVO) technique is based on ranking the pixels within a block and often employed in reversible data hiding (RDH) to preserve the image fidelity. In this paper, an improved PVO-based algorithm is proposed to achieve a high-performance RDH. We formulate the PVO embedding as a problem of multiple histograms modification (MHM) and make two contributions. Firstly, the existing PVO-based algorithms are generalized in an MHM form and the systematic analysis of performance is offered by considering the parameter determination. Secondly, based on the new framework, an improved implementation of PVO embedding is proposed to adaptively determine the MHM manner by solving the distortion minimization of multiple histograms, which can handle different characteristics in image areas. Experimental results show the superiority of the new algorithm over the state-of-the-art methods.     

PVO-based reversible data hiding using adaptive multiple histogram generation and modification

A recently proposed pixel-value-ordering (PVO)–based reversible data hiding (RDH) method takes a sharp histogram derived from two skewed histograms, which realizes good performance through reducing the number of shifted pixels. However, in this recent work, since pixels with different local complexity are processed with the same modification manner based on a single histogram without considering their different local properties, the embedding performance is far from optimal. In this paper, to better exploit the pixel complexity and enhance the reversible embedding performance, a novel PVO-based RDH method using adaptive multiple histogram generation and modification is proposed. First, pixels with different local complexity are divided into several classes utilizing multiple thresholds. Then, a PVO-based predictor is used for prediction and multiple prediction-error histograms corresponding to the different pixel classes are obtained. Next, the generated histograms are modified to embed data according to their statistical characteristics. Here, based on the established capacity–distortion model, the histogram generation and modification are processed in an adaptive way to optimize the embedding performance. Moreover, by extending the proposed method to multiple two-dimensional prediction-error histograms, the embedding performance can be further improved. Experimental results verify that the proposed method outperforms certain state-of-the-art techniques with good marked-image quality.     

Reversible data hiding with automatic contrast enhancement using two-sided histogram expansion

Recently, reversible data hiding (RDH) has emerged into a new class of data hiding methods that enables exact retrieving of both embedded data and cover medium. In the present study, a novel automatic RDH method with contrast enhancement is proposed, in which the data is embedded through two-sided histogram expansion. Two-sided histogram shifting doubles the number of bits embedded at each iteration. Moreover, it preserves the mean brightness of the cover image and prevents it from over enhancement with less calculation. Experimental results on two sets of images show that the proposed method enhances the image contrast at an appropriate level without using a mean brightness controller during data embedding and provides higher information security compared to the existing RDH approaches.