论文标题
基于拓扑的加密3D网格模型的高容量可逆数据隐藏
High Capacity Reversible Data Hiding for Encrypted 3D Mesh Models Based on Topology
论文作者
论文摘要
隐藏在加密域（RDH-ED）中的可逆数据不仅可以保护3D网格模型的隐私并嵌入其他数据，还可以恢复原始模型并无损提取其他数据。但是，由于模型拓扑的使用不足，现有方法在嵌入能力方面尚未获得令人满意的结果。为了进一步提高容量，根据3D网格模型的拓扑提出了一种RDH-ED方法，该方法将顶点分为两个部分：嵌入设置和预测集。整数映射后，嵌入集的嵌入能力由预测集计算。然后将其传递给数据HIDER以嵌入其他数据。最后，可以用正确的键分别提取和恢复其他数据和原始模型。实验声明，与现有方法相比，该方法可以获得最高的嵌入能力。
Reversible data hiding in encrypted domain(RDH-ED) can not only protect the privacy of 3D mesh models and embed additional data, but also recover original models and extract additional data losslessly. However, due to the insufficient use of model topology, the existing methods have not achieved satisfactory results in terms of embedding capacity. To further improve the capacity, a RDH-ED method is proposed based on the topology of the 3D mesh models, which divides the vertices into two parts: embedding set and prediction set. And after integer mapping, the embedding ability of the embedding set is calculated by the prediction set. It is then passed to the data hider for embedding additional data. Finally, the additional data and the original models can be extracted and recovered respectively by the receiver with the correct keys. Experiments declare that compared with the existing methods, this method can obtain the highest embedding capacity.
