Separable Reversible Data Hiding for Encrypted 3D Meshes Based on Self-Organized Blocking and Most Significant Bit Prediction

As a booming technique that allows secret data extraction and information carrier recovery without any loss, reversible data hiding in different carriers has attracted more and more concerns in the field of information security. In this paper, a separable reversible data hiding technique for encrypted 3D meshes is proposed based on self-organized blocking and most significant bit (MSB) prediction. The content-owner traverses all faces of the mesh in the ascending index order. Through self-organized blocking, adjacent vertices are concentrated in different small sets. The central vertex is considered as the reference and the others as embedded vertices in each set. Then, multiple most significant bits between the central vertex and others are adaptively predicted and reserved as embeddable bits for secret data embedding. Because vertex coordinates in each set have a high space correlation and most vertices participate in the prediction process, a huge number of most significant bits can be marked as embeddable bits to embed secret data. Experimental results demonstrate that the proposed method can obtain the highest embedding rate compared with representative methods. To our best knowledge, the average embedding rate of the proposed method is about 28 bits per vertex (bpv) higher than the recently proposed method. Most importantly, instead of recovering meshes with higher quality, original meshes with high visual symmetry/quality can be recovered.

Authors

Institutions

Publication Details

Journal
Symmetry
Published
2024-08-16
DOI
https://doi.org/10.3390/sym16081059
Citations
2
Primary Topic
Advanced Steganography and Watermarking Techniques
Type
article
Field-Weighted Citation Impact
0.50

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Separable Reversible Data Hiding for Encrypted 3D Meshes Based on Self-Organized Blocking and Most Significant Bit Prediction

Zhaolin Xiao, Liansheng Sui, Pengfei Zhang, Nan Zhou
2 citations
Symmetry
Advanced Steganography and Watermarking Techniques
0.50
article

Separable Reversible Data Hiding for Encrypted 3D Meshes Based on Self-Organized Blocking and Most Significant Bit Prediction

Zhaolin Xiao, Liansheng Sui, Pengfei Zhang, Nan Zhou
article en
2 citations

Abstract

As a booming technique that allows secret data extraction and information carrier recovery without any loss, reversible data hiding in different carriers has attracted more and more concerns in the field of information security. In this paper, a separable reversible data hiding technique for encrypted 3D meshes is proposed based on self-organized blocking and most significant bit (MSB) prediction. The content-owner traverses all faces of the mesh in the ascending index order. Through self-organized blocking, adjacent vertices are concentrated in different small sets. The central vertex is considered as the reference and the others as embedded vertices in each set. Then, multiple most significant bits between the central vertex and others are adaptively predicted and reserved as embeddable bits for secret data embedding. Because vertex coordinates in each set have a high space correlation and most vertices participate in the prediction process, a huge number of most significant bits can be marked as embeddable bits to embed secret data. Experimental results demonstrate that the proposed method can obtain the highest embedding rate compared with representative methods. To our best knowledge, the average embedding rate of the proposed method is about 28 bits per vertex (bpv) higher than the recently proposed method. Most importantly, instead of recovering meshes with higher quality, original meshes with high visual symmetry/quality can be recovered.

SymmetryVol. 16(8)
Xi'an Technological University (CN), Xi'an University of Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 36%
Advanced Steganography and Watermarking Techniques
0.50
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Separable Reversible Data Hiding for Encrypted 3D Meshes Based on Self-Organized Blocking and Most Significant Bit Prediction — Zhaolin Xiao, Liansheng Sui, et al. · Symmetry (2024) | TGRS Research Map | TGRS