目的 密文图像可逆数据隐藏技术既可以保证载体内容不被泄露，又可以传递秘密信息，在军事、医疗等方面有着重要的作用。然而，以往的大多数方法存在图像冗余未被充分利用、数据嵌入容量不大等问题。为解决这些问题，提出了一种结合Kd-树和熵编码的高容量密文图像可逆数据隐藏算法。方法 该方法在图像加密之前需要对图像应用中值边缘检测算法计算预测误差，并把得到的预测误差绝对值图像划分为两个区域：S0区域和S1区域。根据Kd-树标签算法和熵编码生成辅助信息，在对图像使用加密密钥K_e加密之后嵌入辅助信息，生成加密图像；在秘密数据嵌入阶段，根据附加信息和数据隐藏密钥嵌入秘密数据，生成载密图像；在解密阶段可以根据附加信息、图像加密密钥和数据隐藏密钥提取秘密数据并无损地恢复图像。结果实验测试了BOWS-2数据集，平均嵌入容量为3.9098bpp。与现有的几种方法进行比较，该算法可以获得更高的秘密数据嵌入容量。结论 该方法在图像加密前腾出空间，与相关算法相比，实现了更高的嵌入容量，并且可以实现原始图像的无损恢复。

Objective Nowadays, many people upload their information to the Internet, but there are many security problems in the transmission and storage process. In the early days, researchers used encryption technology to protect the information, but it was easy to get the attention of decipherers. Therefore, people began to study how to hide secret information in the image. The secret information is transmitted while avoiding the attention of potential attackers. Therefore, the reversible data hiding technology has become one of the hotspots of security research. Reversible data hiding technology can embed secret data through subtle modifications to the original image. After the data is extracted, the image can be restored losslessly. With the rise of cloud storage and big data technology, many users upload their images to the cloud server. Out of distrust of the service provider, the image will be encrypted before uploading to the cloud server. Cloud storage service providers hope to embed additional data in images to facilitate image management, image retrieval, copyright protection and other requirements. Therefore, for cloud applications, the reversible data hiding technology in encrypted images (RDHEI) has attracted the attention of a large number of researchers, hoping to embed data in encrypted images for transmission, so that it can also better protect the carrier image and ensure the security of embedded information. Depending on whether it is necessary to vacate the space before the image is encrypted, the existing RDHEI methods can be divided into two categories: 1) vacating room after encryption (VRAE), 2) reserving room before encryption (RRBE). Reversible data hiding technology for encrypted images plays an important role in military, medical, and other aspects. This algorithm can not only ensure that the content of the carrier is not leaked, but also transmit secret information. However, most previous methods have problems such as low data embedding capacity, errors in data extraction, and poor visual quality of reconstructed images. To solve these problems, a reversible data hiding algorithm for high capacity ciphertext images based on Kd-tree and entropy coding is proposed. Method This method needs preprocessing before image encryption. First, The MED predictor generates a prediction error absolute value image from the original image, and the prediction error absolute value image is divided into two regions, i.e., S0 region and S1 region. The S0 region contains the 5th bit plane to the most significant bit plane, and the Kd-tree algorithm is used to construct the Kd-tree concept subtree marks the blocks of the four bits planes to determine whether the blocks can accommodate secret bits. The S1 region is from the least significant bit plane to the 4th bit plane, and the bitstream of each bit plane is compressed using arithmetic coding. The remaining space can be used to embed secret data. After the image is encrypted with the encryption key, the additional information is embedded to generate the encrypted image. During the stage of secret data embedding, the secret data is embedded according to the additional information and data hiding key to generate the secret image. In the decoding stage, the secret data is extracted and the image is restored losslessly according to the additional information, encryption key and data hiding key. Result Experiments show that the proposed method can effectively reduce the number of reference pixels and additional information, thereby increasing the data embedding rate. The BOWS-2 data set is tested in the experiment. The average embedding capacity is 3.9098 bpp, which is higher than the existing five methods. Two indicators, peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), are used to evaluate the similarity between the original image and the restored image. Experimental results prove that in the stage of data extraction and image restoration, after extracting the secret data and using the image encryption key to decrypt the image, there is no difference between the original image. The analysis of Kd-tree label through encryption shows that the complexity of texture has a significant impact on the embedding of secret data of the image. The higher the label provided by the relatively smooth image, the higher the embedding capacity. Conclusion First, the image pixels are predicted by predictor, and then the image pixels are classified and divided into two regions. This method adopts the framework of reserving room before encryption. The image needs to be pre-processed before image encryption. Compared with related algorithms, it achieves a higher embedding capacity. It can not only achieve perfect reconstruction of the original image, but also ensure security of encrypted images and additional data. At present, many disciplines are combined with deep learning, but there are almost no researches that combine deep learning with reversible data hiding algorithms in the encrypted domain. In the future, we hope to achieve breakthroughs in this area, and pay more attention to the application of RDHEI in reality, not just academic research.