基于块分类的密文域多重嵌入可逆信息隐藏算法

针对现有密文域可逆信息隐藏方法中图像块利用不充分，使得嵌入秘密信息量不高的问题，提出了一种基于块分类的多重嵌入可逆信息隐藏算法。首先将原始图像用流密码加密，加密图像被分成若干个不重叠的块。然后，用最高有效位（most significant bit, MSB）自适应预测算法对块内的第1个像素和其他像素进行预测，将每一个块标记为可用块或非可用块。进一步对可用块进行重构嵌入，同时用中值边缘检测器（median-edge detector, MED）预测算法对非可用块进行二次嵌入，最终实现秘密信息的嵌入。当接收方接收到含密图像时，通过嵌入密钥实现秘密信息的正确提取，同时利用加密密钥恢复原始图像。实验证明，该文提出的方法在相同图像恢复质量的情况下能够显著提升秘密信息的嵌入量，在嵌入容量和图像恢复质量上均优于已有的方法。

Reversible Information Hiding Algorithm in Ciphertext Domain with Multiple Embedding Based on Block Classification

Insufficient utilization of image blocks in the existing reversible information hiding methods results in a low embedding capacity of secret information. In order to address this issue, this paper proposes a multiple embedding reversible information hiding algorithm based on block classification. First, the original image is encrypted with a stream cipher, and the encrypted image is further divided into multiple non-overlapping blocks. Subsequently, the Most Significant Bit (MSB) adaptive prediction algorithm is used to predict the first pixel and other pixels in each block, which is marked as a usable block or an unusable block. Finally, the available blocks are reconstructed and embedded with secret data, and the non-available blocks are re-embedded with the Median-Edge Detector (MED) prediction algorithm to realize the embedding of secret information. When the receiver receives the secret image, the secret information is extracted by the data-hiding key, and the original image is restored by the encryption key. Experimental results demonstrate that the proposed method can significantly improve the embedding capacity of secret information embedding, while keeping a high image restoration quality. The overall performance is superior to existing methods in terms of both embedding capacity and image restoration quality.