High Capacity Reversible Data Hiding in Encrypted 3D Mesh Models Based on Multi-MSB Prediction

TL;DR

This paper introduces a high-capacity reversible data hiding method for encrypted 3D mesh models that uses Multi-MSB prediction and auxiliary information compression to improve embedding capacity and restore quality.

Contribution

It proposes a novel approach dividing mesh vertices into sets, utilizing Multi-MSB prediction and arithmetic coding, achieving higher capacity and quality in reversible data hiding for 3D meshes.

Findings

Achieves higher embedding capacity than existing methods.

Restores original 3D mesh models with high quality.

Effective use of Multi-MSB prediction and auxiliary compression.

Abstract

As a new generation of digital media for covert transmission, three-dimension (3D) mesh models are frequently used and distributed on the network. Facing the huge massive of network data, it is urgent to study a method to protect and store this large amounts of data. In this paper, we proposed a high capacity reversible data hiding in encrypted 3D mesh models. This method divides the vertices of all 3D mesh into "embedded sets" and "prediction sets" based on the parity of the index. In addition, the multiple most significant bit (Multi-MSB) prediction reserved space is used to adaptively embed secret message, and the auxiliary information is compressed by arithmetic coding to further free up redundant space of the 3D mesh models. We use the majority voting system(MSV) principle to restore the original mesh model with high quality. The experimental results show that our method achieves a higher embedding capacity compared with state-of-the-art RDH-ED methods on 3D mesh models and can restore the original 3D mesh models with high quality.