Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

High Capacity Reversible Data Hiding for Encrypted 3D Mesh Models Based on Topology

Yun Tang, Lulu Cheng, Wanli Lyv, Zhaoxia Yin

TL;DR

The paper addresses high-capacity reversible data hiding in encrypted 3D mesh models by introducing a topology-aware framework that partitions vertices into embedding and prediction sets, uses integer mapping for coordinates, and records prediction errors to maximize payload capacity. Data is encrypted with a stream cipher and embedded into the encrypted mesh, enabling lossless data extraction and original-model recovery when the correct keys are available, with separable extraction and recovery. Experimental results show state-of-the-art embedding rates and preserved visual fidelity on several meshes and the Princeton dataset, indicating practical applicability for privacy-preserving 3D mesh data hiding. The work highlights the advantages of leveraging model topology to boost capacity while maintaining low computational cost compared to homomorphic approaches.

Abstract

Reversible data hiding in encrypted domain(RDH-ED) can not only protect the privacy of 3D mesh models and embed additional data, but also recover original models and extract additional data losslessly. However, due to the insufficient use of model topology, the existing methods have not achieved satisfactory results in terms of embedding capacity. To further improve the capacity, a RDH-ED method is proposed based on the topology of the 3D mesh models, which divides the vertices into two parts: embedding set and prediction set. And after integer mapping, the embedding ability of the embedding set is calculated by the prediction set. It is then passed to the data hider for embedding additional data. Finally, the additional data and the original models can be extracted and recovered respectively by the receiver with the correct keys. Experiments declare that compared with the existing methods, this method can obtain the highest embedding capacity.

High Capacity Reversible Data Hiding for Encrypted 3D Mesh Models Based on Topology

TL;DR

The paper addresses high-capacity reversible data hiding in encrypted 3D mesh models by introducing a topology-aware framework that partitions vertices into embedding and prediction sets, uses integer mapping for coordinates, and records prediction errors to maximize payload capacity. Data is encrypted with a stream cipher and embedded into the encrypted mesh, enabling lossless data extraction and original-model recovery when the correct keys are available, with separable extraction and recovery. Experimental results show state-of-the-art embedding rates and preserved visual fidelity on several meshes and the Princeton dataset, indicating practical applicability for privacy-preserving 3D mesh data hiding. The work highlights the advantages of leveraging model topology to boost capacity while maintaining low computational cost compared to homomorphic approaches.

Abstract

Reversible data hiding in encrypted domain(RDH-ED) can not only protect the privacy of 3D mesh models and embed additional data, but also recover original models and extract additional data losslessly. However, due to the insufficient use of model topology, the existing methods have not achieved satisfactory results in terms of embedding capacity. To further improve the capacity, a RDH-ED method is proposed based on the topology of the 3D mesh models, which divides the vertices into two parts: embedding set and prediction set. And after integer mapping, the embedding ability of the embedding set is calculated by the prediction set. It is then passed to the data hider for embedding additional data. Finally, the additional data and the original models can be extracted and recovered respectively by the receiver with the correct keys. Experiments declare that compared with the existing methods, this method can obtain the highest embedding capacity.
Paper Structure (16 sections, 9 equations, 6 figures, 5 tables)

This paper contains 16 sections, 9 equations, 6 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Proposed Method
  4. Room Reservation
  5. Vertices Division
  6. Integer Mapping
  7. Prediction Error Bit Record
  8. Model Encryption
  9. Data Embedding
  10. Data Extraction and Model Recovery
  11. Experiment Results and Analysis
  12. Visual Quality and Quantitative Analysis
  13. Capacity Analysis
  14. Features Analysis
  15. Conclusion
  16. ...and 1 more sections

Figures (6)

  • Figure 1: Method flowchart.
  • Figure 2: (a) Mushroom, (b) The labeled local topology of Mushroom.
  • Figure 3: Prediction error labels for $v_1$ in $x$-axis.
  • Figure 4: The process of data embedding.
  • Figure 5: Models presentation at different stages($p$=5): (a) original models; (b) encrypted models; (c) data-embedded models; (d) recovery models.
  • ...and 1 more figures