Full Text:
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CLC number: TP391
On-line Access: 2025-02-10
Received: 2024-05-07
Revision Accepted: 2024-06-24
Crosschecked: 2025-02-18
Cited: 0
Clicked: 47
Citations: Bibtex RefMan EndNote GB/T7714
Algorithm for 3D point cloud steganalysis based on composite operator feature enhancement
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Shuai REN, Hao GONG, Suya ZHENG. Algorithm for 3D point cloud steganalysis based on composite operator feature enhancement[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(1): 62-78.
@article{title="Algorithm for 3D point cloud steganalysis based on composite operator feature enhancement",
author="Shuai REN, Hao GONG, Suya ZHENG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="26",
number="1",
pages="62-78",
year="2025",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2400360"
}
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%T Algorithm for 3D point cloud steganalysis based on composite operator feature enhancement
%A Shuai REN
%A Hao GONG
%A Suya ZHENG
%J Frontiers of Information Technology & Electronic Engineering
%V 26
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%P 62-78
%@ 2095-9184
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2400360
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T1 - Algorithm for 3D point cloud steganalysis based on composite operator feature enhancement
A1 - Shuai REN
A1 - Hao GONG
A1 - Suya ZHENG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 26
IS - 1
SP - 62
EP - 78
%@ 2095-9184
Y1 - 2025
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2400360
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.
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