Algorithm for 3D point cloud steganalysis based on composite operator feature enhancement

Abstract

Three-dimensional (3D) point cloud information hiding algorithms are mainly concentrated in the spatial domain. Existing spatial domain steganalysis algorithms are subject to more disturbing factors during the analysis and detection process, and can only be applied to 3D mesh objects, so there is a lack of steganalysis algorithms for 3D point cloud objects. To change the fact that steganalysis is limited to 3D mesh and eliminate the redundant features in the 3D mesh steganalysis feature set, we propose a 3D point cloud steganalysis algorithm based on composite operator feature enhancement. First, the 3D point cloud is normalized and smoothed. Second, the feature points that may contain secret information in 3D point clouds and their neighboring points are extracted as the feature enhancement region by the improved 3DHarris-ISS composite operator. Feature enhancement is performed in the feature enhancement region to form a feature-enhanced 3D point cloud, which highlights the feature points while suppressing the interference created by the rest of the vertices. Third, the existing 3D mesh feature set is screened to reduce the data redundancy of more relevant features, and the newly proposed local neighborhood feature set is added to the screened feature set to form the 3D point cloud steganography feature set POINT72. Finally, the steganographic features are extracted from the enhanced 3D point cloud using the POINT72 feature set, and steganalysis experiments are carried out. Experimental analysis shows that the algorithm can accurately analyze the 3D point cloud’s spatial steganography and determine whether the 3D point cloud contains hidden information, so the accuracy of 3D point cloud steganalysis, under the prerequisite of missing edge and face information, is close to that of the existing 3D mesh steganalysis algorithms.

摘要

三维点云信息隐藏算法主要集中在空间域。现有的空间域隐写分析算法在分析检测过程中受干扰因素较多,且仅能应用于三维网格对象,缺少针对三维点云对象的隐写分析算法。为打破隐写分析仅限于三维网格的局限,消除三维网格隐写分析特征集中的冗余特征,提出基于复合算子特征增强的三维点云隐写分析算法。首先,对三维点云进行归一化以及平滑处理。其次,通过改进的3DHarris-ISS复合算子提取三维点云中可能含密的特征点以及其邻域点作为特征增强区域,并在特征增强区域进行特征增强,形成特征增强的三维点云,在突出特征点的同时抑制其余顶点带来的干扰。再次,筛选已有的三维网格特征集合,减少更多相关特征的数据冗余,并将新提取的局部邻域特征集添加到筛选的特征集,从而形成三维点云隐写分析特征集POINT72。最后,利用POINT72特征集对增强后的三维点云进行隐写特征提取,并进行隐写分析实验。实验分析表明,算法可以准确分析三维点云的空域隐写,并判断三维点云是否含有隐藏信息。在缺少边信息和面信息的前提下,三维点云隐写分析的准确率接近现有三维网格隐写分析算法。