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.
翻译:加密域(RDH-ED)中隐藏的可变数据不仅可以保护 3D 网目模型的隐私和嵌入额外数据的隐私,还可以回收原始模型和不遗漏地提取更多数据。然而,由于模型地形学使用不足,现有方法在嵌入能力方面没有取得令人满意的结果。为了进一步提高能力,根据3D 网目模型的地形学提出了RDH-ED方法,该方法将脊椎分为两个部分:嵌入数据集和预测数据集。在整数绘图后,嵌入数据集的嵌入能力由预测数据集计算。然后通过数据隐藏器来嵌入更多数据。最后,额外数据和原始模型可以分别由正确的密钥接收器提取和回收。实验表明,与现有方法相比,这种方法可以获得最高的嵌入能力。