Separable Reversible Data Hiding for Encrypted 3D Mesh Models Based on Octree Subdivision and Multi-MSB Prediction
Separable Reversible Data Hiding for Encrypted 3D Mesh Models Based on Octree Subdivision and Multi-MSB Prediction
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DOI：
10.1109/tmm.2023.3295578
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发表时间：
2024
影响因子：
7.3
通讯作者：
Gangyang Hou;Bo Ou;Min Long;Fei Peng
中科院分区：
文献类型：
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作者：
Gangyang Hou;Bo Ou;Min Long;Fei Peng
加密域可逆数据隐藏技术（RDH-ED）可以对数据进行加密以实现原始媒体的隐私保护，并嵌入额外的数据以实现隐蔽通信或访问控制。然而，目前的研究主要集中在加密的图像，而很少关注加密的三维（3D）模型。本文提出了一种基于八叉树空间剖分和多最高有效位预测的高容量可分离的三维加密模型RDH-ED方法。该算法首先利用八叉树空间剖分方法将三维模型自适应地划分为互不重叠的子块，并将子块中的顶点划分为嵌入集和参考集。为了更好地利用两个集合的空间相关性，利用嵌入集合的多MSB预测误差嵌入附加数据，利用参考集合对嵌入集合进行有损恢复。然后，通过指定的加密算法对模型进行加密。最后，通过多MSB替换将附加数据嵌入到预留的嵌入空间中。实验结果表明，与现有方法相比，该方法能够获得更高的嵌入容量，并保证三维模型的无损恢复。
Reversible data hiding in encrypted domain (RDH-ED) can perform data encryption to fulfill the privacy protection of original media and embed additional data for covert communication or access control. However, current researches are focusing on the encrypted images, and little attention is paid to encrypted three-dimensional (3D) models. In this article, a high capacity separable RDH-ED method for encrypted 3D models is proposed based on octree spatial subdivision and multiple most significant bit (multi-MSB) prediction. Firstly, a 3D model is adaptively subdivided into non-overlapping subblocks by octree spatial subdivision, and the vertices in a subblock are classified into embedding set and reference set. To better utilize the spatial correlation of the two sets, the multi-MSB prediction error of the embedding set is used to embed the additional data, and the reference set is used to losslessly recover the embedded set. Then, the model is encrypted by a specified encrypted algorithm. At last, additional data is embedded into the reserved embedding room by multi-MSB substitution. Experimental results show that the proposed method can achieve a higher embedding capacity compared with the state-of-the-art methods, and guarantee the lossless recovery of the 3D model.