Abstract: Aiming at the problem that some 3D model-based information hiding algorithms are incompetent against combined attacks, a new strategy based on feature point labeling and clustering was proposed. Firstly, edge folding was adopted to achieve mesh simplification and all the vertexes were labeled in order by their energy level. Secondly, the ordered vertexes were clustered and re-ordered by using local height theory and Mean Shift clustering analysis. Lastly, hidden information and cover model carrier information were optimized, matched and modified by Logistic chaos mapping scrambling and genetic algorithm, completing the final hiding. The data in hiding areas were labeled and screened locally and globally according to the energy weight, which is good for the robustness and transparency of the algorithm. The experimental results show that, compared with 3D information hiding algorithms based on inscribed sphere and outer skeleton, the robustness of the proposed algorithm against single or joint attacks is significantly improved, and it also has the same degree of invisibility.

Key words: information hiding, mesh simplification by edge folding, local height theory, Mean Shift clustering analysis, 3D model

摘要： 针对三维模型信息隐藏无法有效抵抗联合攻击问题，提出一种新的基于特征点标注与聚类的隐藏算法。首先对三维模型进行边折叠操作，对其全部顶点进行标注排序；其次，利用局部高度理论和Mean Shift聚类分析算法对标注排序后的顶点进行能量划分；最后，利用Logistic混沌映射置乱和遗传算法对隐藏信息和载体信息进行优化匹配与修改，实现信息的最终隐藏。隐藏区域的数据按照能量权重进行了全局和局部的标注与筛选，有利于算法的鲁棒性和不可见性。实验结果表明，与基于三维内切球和轮廓标注的三维模型信息隐藏算法相比，该算法抵御单个或联合攻击的鲁棒性有明显的提高，且具有几乎相同程度的不可见性。

关键词: 信息隐藏, 边折叠网格简化, 局部高度理论, Mean Shift聚类分析, 三维模型