Reversible data hiding in encrypted image (RDHEI) is a technique that can be adopted by cloud sever to embed additional data into the encrypted image with no permanent distortion. For RDHEI, it remains a challenging task to improve the embedding capacity under the premise of real reversibility. In this paper, a novel RDHEI method based on adaptive most significant bit (MSB) prediction is proposed. The cover image is first encrypted in block-wise manner such that the correlation of pixels within block is preserved. Next, all blocks are permuted to fulfill the final encryption. During data embedding, the upper-left pixel within block is used to predict others such that the embedding room is vacated. Then, available blocks are selected and all blocks are rearranged so as to ensure reversibility. By fully exploiting the correlation of pixels within block via adaptive MSB prediction, the proposed method successes to achieve desirable improvement in capacity. Experimental results show that the proposed method significantly outperforms previous methods. Moreover, real reversibility and real separability are also guaranteed. In a word, the proposed method is a practical method that can be adopted by cloud storage.

The cover image is generally encrypted by a stream cipher in existing reversible data hiding in encrypted image (RDHEI) methods. As pixel correlation is seriously damaged, more than one pixels have to be employed to carry one bit such that quite limited capacity is achieved. To overcome this issue, a high capacity RDHEI method which preserves pixel correlation and exploits it to vacate embedding room is proposed in this paper. First, a block-level encryption method which combines block-level stream cipher and block-level permutation is proposed. With the correlation of pixels in block preserved, all blocks are classified into usable blocks (UBs) and unusable blocks (NUBs). Then, UB is reconstructed to vacate room for data embedding owing to pixels in block sharing the same most significant bits (MSBs). To ensure reversibility, the number of NUBs between current UB and the previous one is also embedded along with additional data, and blocks are rearranged in a reversible way such that UBs are always in front of NUBs. Experimental results show that not only the embedding capacity is significantly improved but also the hidden data can be losslessly extracted, and the cover image can be perfectly recovered.

In reversible watermarking for image authentication, less degradation of the marked image is always desirable. For minimum distortion, the pairwise prediction-error expansion (PEE) technique was recently proposed to modify errors jointly. Although its superiority over conventional PEE has been verified, its potential has not been fully exploited yet. In this paper, we focus on optimal modification and propose an enhanced pairwise PEE. First, it is observed in PVO-based pairwise PEE that the histogram peak varies with relative location when predicting the largest/smallest two pixels. Then, a more effective 2D mapping is proposed by content-dependently selecting the expansion bin after introducing spatial location into prediction. Next, the 2D mapping is further extended considering prediction in non-smooth region tends to produce errors with large magnitude. Finally, we also propose to flexibly define the spatial location to achieve content-dependent prediction and further enhancement. Experimental results demonstrate that the proposed scheme achieves better capacity-distortion trade-off and outperforms several state-of-the-art schemes.

Pixel value ordering prediction has been verified as an effective mechanism to exploit image redundancy for reversible data hiding (RDH) and numerous extensions have been devised. However, their performance is still unsatisfactory since the error modification is generally fixed and independent of image content. In this paper, a new RDH scheme is proposed by incorporating pixel distance to realize adaptive multiple histograms modification (AMHM). During exploiting the correlation between the largest/smallest pixel and any other one in the scope of pixel block, we propose to process every two correlated pixels successively following the ascending order of their distance. Specifically, the generated errors with a given distance are collected and verified. If they are all shiftable errors, the follow-up errors would be collected into the next sub-histogram. In this way, a histogram sequence is adaptively generated such that different modification mechanisms can be taken for different sub-histograms to achieve adaptive embedding. Finally, AMHM for conventional prediction-error expansion (PEE) and AMHM for 2D PEE have been both realized in this paper. Experimental results show that AMHM is of great significance to better exploit pixel correlation and the proposed scheme outperforms a series of the latest schemes.