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Reversible data hiding in encrypted images (RDHEI) is an effective technique of data security. Most state-of-the-art RDHEI methods do not achieve desirable payload yet. To address this problem, we propose a new RDHEI method with hierarchical embedding. Our contributions are twofold. (1) A novel technique of hierarchical label map generation is proposed for the bit-planes of plaintext image. The hierarchical label map is calculated by using prediction technique, and it is compressed and embedded into the encrypted image. (2) Hierarchical embedding is designed to achieve a high embedding payload. This embedding technique hierarchically divides prediction errors into three kinds: small-magnitude, medium-magnitude, and large-magnitude, which are marked by different labels. Different from the conventional techniques, pixels with small-magnitude/large-magnitude prediction errors are both used to accommodate secret bits in the hierarchical embedding technique, and therefore contribute a high embedding payload. Experiments on two standard datasets are discussed to validate the proposed RDHEI method. The results demonstrate that the proposed RDHEI method outperforms some state-of-the-art RDHEI methods in payload. The average payloads of the proposed RDHEI method are 3.4568 bpp and 3.6823 bpp for BOWS-2 dataset and BOSSbase dataset, respectively. [ABSTRACT FROM AUTHOR]

Reversible data hiding (RDH) is a useful technique of data security. Embedding capacity is one of the most important performance of RDH for encrypted image. Many existing RDH algorithms for encrypted image do not reach desirable embedding capacity yet. To address this problem, a new RDH algorithm is proposed for encrypted image based on adaptive prediction error coding. The proposed RDH algorithm uses a block‐based encryption scheme to preserve spatial correlation of original image in the encrypted domain and exploits a novel technique called adaptive prediction error coding to vacate room for data embedding. A key contribution of the proposed RDH algorithm is the adaptive prediction error coding. It can efficiently vacate room from encrypted image block by adaptively coding prediction errors according to block content and thus contributes to a large embedding capacity. Many experiments on benchmark image databases are done to validate performance of the proposed RDH algorithm. The results show that the average embedding rates on the open databases of UCID, BOSSBase and BOWS‐2 are 1.7081, 2.4437 and 2.3083 bpp, respectively. Comparison results illustrate that the proposed RDH algorithm outperforms some state‐of‐the‐art RDH algorithms in embedding capacity. [ABSTRACT FROM AUTHOR]

Most data hiding methods have limitations in resisting cropping and noise attacks. Aiming at this problem, a robust data hiding with multiple backups and optimized reference matrix is proposed in this paper. Specifically, secret data is divided into a set of groups and multiple backups of each group data are generated according to the number of backups. The cover image is divided into several blocks. A reference matrix is constructed by four constraints to assist data hiding and data extraction. The proposed method aims to extract exactly at least one backup of each group data so that the correct backups can construct the secret data well if the stego-image is corrupted. Experimental results show that the proposed algorithm is robust to cropping and noise attacks. [ABSTRACT FROM AUTHOR]

Data hiding in encrypted image is a recent popular topic of data security. In this paper, we propose a reversible data hiding algorithm with pixel prediction and additive homomorphism for encrypted image. Specifically, the proposed algorithm applies pixel prediction to the input image for generating a cover image for data embedding, referred to as the preprocessed image. The preprocessed image is then encrypted by additive homomorphism. Secret data is finally embedded into the encrypted image via modular 256 addition. During secret data extraction and image recovery, addition homomorphism and pixel prediction are jointly used. Experimental results demonstrate that the proposed algorithm can accurately recover original image and reach high embedding capacity and good visual quality. Comparisons show that the proposed algorithm outperforms some recent algorithms in embedding capacity and visual quality. [ABSTRACT FROM AUTHOR]