Separable Reversible Data Hiding Based on Integer Mapping and Multi-MSB Prediction for Encrypted 3D Mesh Models

  • Zhaoxia Yin*
  • , Na Xu
  • , Feng Wang
  • , Lulu Cheng
  • , Bin Luo
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

25 Scopus citations

Abstract

Extensive research has been conducted on image-based reversible data hiding in encrypted domain (RDH-ED) methods, but these methods cannot be directly applied to other cover medium, such as text, audio, video, and 3D mesh. With the widespread use of 3D mesh on the Internet, the use of 3D mesh as cover medium for RDH has gradually become a research topic. The main challenge of studying RDH based on 3D mesh is that the data structure of 3D mesh is complex and the geometric structure is irregular. In this paper, we propose a separable RDH-ED method based on integer mapping and multiple most significant bit (Multi-MSB) prediction. Firstly, all vertices of 3D mesh are divided into “embedded” set and “reference” set, and floating-point vertex values are mapped to integers. Then, sender calculates prediction error of the “embedded” set. Data hider embeds additional data by replacing the Multi-MSB of the encrypted vertex coordinates of the “embedded” set without prediction error. According to different permissions, legal recipients can obtain the original mesh, the additional data or both of them by using the proposed separable method. Experimental results prove that the proposed method outperforms state-of-the-art methods.

Original languageEnglish
Title of host publicationPattern Recognition and Computer Vision - 4th Chinese Conference, PRCV 2021, Proceedings
EditorsHuimin Ma, Liang Wang, Changshui Zhang, Fei Wu, Tieniu Tan, Yaonan Wang, Jianhuang Lai, Yao Zhao
PublisherSpringer Science and Business Media Deutschland GmbH
Pages336-348
Number of pages13
ISBN (Print)9783030880064
DOIs
StatePublished - 2021
Externally publishedYes
Event4th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2021 - Beijing, China
Duration: 29 Oct 20211 Nov 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13020 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2021
Country/TerritoryChina
CityBeijing
Period29/10/211/11/21

Keywords

  • 3D mesh
  • Encrypted domain
  • Multi-MSB prediction
  • Reversible data hiding

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