High Capacity Reversible Data Hiding in Encrypted 3D Mesh Models Based on Multi-MSB Prediction
This addresses the need for secure covert transmission and storage of 3D mesh models in network data, representing an incremental improvement in reversible data hiding techniques for this domain.
The paper tackles the problem of protecting and storing large amounts of 3D mesh model data by proposing a high-capacity reversible data hiding method in encrypted 3D mesh models, achieving higher embedding capacity compared to state-of-the-art methods and restoring original models with high quality.
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.