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

IF 2.7 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Shuai Ren, Hao Gong, Suya Zheng
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引用次数: 0

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.

基于复合算子特征增强的三维点云隐写分析算法
三维(3D)点云信息隐藏算法主要集中在空间域。现有的空间域隐分析算法在分析检测过程中受到的干扰因素较多,且只能应用于三维网格对象,因此缺乏针对三维点云对象的隐分析算法。为了改变隐分析仅限于三维网格的现状,消除三维网格隐分析特征集中的冗余特征,我们提出了一种基于复合算子特征增强的三维点云隐分析算法。首先,对三维点云进行归一化和平滑处理。其次,通过改进的 3DHarris-ISS 复合算子提取三维点云中可能包含秘密信息的特征点及其邻近点作为特征增强区域。在特征增强区域内进行特征增强,形成特征增强三维点云,突出特征点,同时抑制其他顶点产生的干扰。第三,对现有的三维网格特征集进行筛选,以减少更多相关特征的数据冗余,并将新提出的局部邻域特征集添加到筛选后的特征集中,形成三维点云隐写特征集 POINT72。最后,利用 POINT72 特征集从增强的三维点云中提取隐写特征,并进行隐写分析实验。实验分析表明,该算法能准确分析三维点云的空间隐写术,并判断三维点云是否包含隐藏信息,因此在缺失边缘信息和人脸信息的前提下,三维点云隐写分析的准确性接近于现有的三维网格隐写分析算法。
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来源期刊
Frontiers of Information Technology & Electronic Engineering
Frontiers of Information Technology & Electronic Engineering COMPUTER SCIENCE, INFORMATION SYSTEMSCOMPU-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
6.00
自引率
10.00%
发文量
1372
期刊介绍: Frontiers of Information Technology & Electronic Engineering (ISSN 2095-9184, monthly), formerly known as Journal of Zhejiang University SCIENCE C (Computers & Electronics) (2010-2014), is an international peer-reviewed journal launched by Chinese Academy of Engineering (CAE) and Zhejiang University, co-published by Springer & Zhejiang University Press. FITEE is aimed to publish the latest implementation of applications, principles, and algorithms in the broad area of Electrical and Electronic Engineering, including but not limited to Computer Science, Information Sciences, Control, Automation, Telecommunications. There are different types of articles for your choice, including research articles, review articles, science letters, perspective, new technical notes and methods, etc.
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