目的 针对现有的加密域可逆信息隐藏算法未能充分利用图像的全部位平面的问题，提出了一种密文域高嵌入率图像全位面可逆数据隐藏。方法 对载体图像进行加密，然后将隐蔽信息嵌入到加密图像中，进行隐蔽传输，发送给接收者。本文将灰度图像的8个位平面都用来进行数据嵌入，并把每个位平面划分成不重叠的块，分为非连续块（块内像素值0，1都存在）和连续块（块内为全0或全1像素值），按块进行重排列且将排列前的块标签嵌入到重排列图像中，使用流密码对图像进行加密。在数据嵌入阶段，提出了带修正信息的像素预测方法用于非连续块的嵌入。连续块中，保持块内右下角像素值不变，用于连续块的恢复，其他位置嵌入数据；非连续块中，对预测正确的像素嵌入数据，预测错误的像素保持不变。结果 实验过程实现了多种密文域可逆数据隐藏算法，本文进行大量对比实验，并在BOSSbase和BOWS-2数据集上进行验证，与其他方法比较，本文方法在BOSSbase和BOWS-2数据集上的嵌入率分别提升了42.1%和43.3%。结论 提出的加密图像可逆数据隐藏方案，通过对不同性质的块采用不同方法进行数据嵌入，利用图像全位面信息，使得方案能够获得更高的嵌入率，表明了本文方法的有效性。

Objective In recent years, reversible data hiding (RDH) of encrypted images has attracted considerable attention. The data hider embeds hidden information into a digital image for covert transmission. However, data embedding often causes damage to the original image, which might not be fully recovered after covert transmission. Thus, effectively combining encryption technology and RDH technology for covert transmission of data is necessary. This combinatorial method ensures that images are encrypted and recoverable. Therefore, RDH technology in the ciphertext domain has become a research focus. The algorithms can be roughly divided into two categories:vacating room after encryption(VRAE) and reserving room before encryption(RRBE). The general method of RDH is typically to encrypt a cover image first, then embed hidden information into the encrypted image, and send it to the receiver. The receiver extracts the secret information and decrypts the encrypted image by using the data hiding key and encryption key, respectively. In previous RDH methods, encrypted images have little redundant space, the bit plane utilization of images is low, the embedding capacity is small, and some flipped pixels may lead to image distortion. Method In this work, the proposed scheme is mainly divided into the following steps:image preprocessing, block tag embedding, image rearrangement, image encryption, data embedding, data extraction, and image decryption. The content owner divides a grayscale image into eight bit planes, each of which is used for data embedding. The pixel value on each bit plane can be viewed as a binary number, and each bit plane is divided into non-overlapping blocks (such as 4×4), which are divided into discontinuous blocks (with pixel values of 0 and 1) and continuous blocks (with pixel values of 0 or 1). An image is rearranged by blocks, and the original order of block labels is embedded in the rearranged image. At the same time, the content owner makes pixel prediction on all discontinuous blocks of all bit planes and obtains the prediction map. Images after arrangement are encrypted with a stream cipher. The content owner sends the encrypted image along with the prediction map to the data hider. In the data embedding phase, the data hider embeds the data according to a pixel prediction method. For a continuous block, the bottom right pixel is kept unchanged for the recovery of the block, and data are embedded in other positions. As a result, the embedding space of each continuous block is very large. For a discontinuous block, a prediction map is generated, and then a pixel prediction model is used. When the prediction is correct, the corresponding value of the prediction map is 1; otherwise, it is 0. In the discontinuous block, only when the prediction map value is 1, data is embedded at the predicted pixel; otherwise, the predicted pixel remains unchanged without embedding data. Of note, all secret data should be encrypted before embedding. Due to the above data embedding phase, cover images can be restored completely later. The embedding capacity of the discontinuous block is less than that of the continuous block. However, due to the large number of discontinuous blocks in the low bit planes, the embedding capacity of discontinuous blocks is considerable in the whole image. However, in some state-of-the-art schemes, the utilization of low bit planes is not sufficient. The proposed scheme solves the problem by using the pixel prediction model with correction information. The data hider sends marked and encrypted images to the receiver. In the image decryption and data extraction stage, the receiver performs image decryption and data extraction according to different keys. Result In the experimental process, various RDH algorithms in the ciphertext domain were used, and a large number of comparison experiments were conducted on the eight grayscale test images to comprehensively compare the two aspects of complete reversibility and embedding efficiency. At the same time, a verification experiment was conducted on the BOSSbase and BOWS-2 dataset. Compared with some state-of-the-art schemes, the embedding rate of the scheme in this study was improved by 42.1% on the BOSSbase dataset and 43.3% on the BOWS-2 dataset. The embedding rate of the proposed scheme reached 3.089 5 and 2.932 0 bit per pixel on the BOSSbase and BOWS-2 dataset, respectively. Compared with other schemes, the proposed scheme embeds data on all bit planes. Unlike other state-of-the-art schemes, the proposed scheme can embed data in the low bit planes because of the pixel prediction method with correction information. Other schemes ignore the contribution of low bit planes to data embedding. Conclusion The proposed scheme provides a large space for embedding additional information and ensures its security. Moreover, it can achieve higher embedding rate, and the image recovery is quite correct by embedding data in different blocks with different ways and using all bit planes of images, which shows the effectiveness of the proposed scheme. The experimental results show that the embedding performance of the proposed scheme is superior to that of other state-of-the-art schemes for the RDH of encrypted images. In future work, we will focus on improving the embedding efficiency of discontinuous blocks and improving the algorithm so as to increase the embedding capacity of low bit planes.