Reversible data hiding for 3D mesh models based on spatial polygon prediction and dual sorting.

Area of Science:

• Computer Science
• Digital Media Security

Background:

• Reversible Data Hiding (RDH) is vital for digital media copyright protection and integrity verification.
• Existing RDH research primarily focuses on 2D images, with limited exploration of 3D models.

Purpose of the Study:

• To propose a novel Reversible Data Hiding (RDH) method specifically for 3D mesh models.
• To address the gap in RDH research for 3D data and improve embedding capacity and data integrity.

Main Methods:

• A three-layer vertex division mechanism for a two-round embedding process to increase embedding capacity.
• A spatial polygon prediction scheme to enhance prediction accuracy while maintaining embedding capacity.
• A dual sorting strategy considering angular smoothness and edge-length regularity for embedding into smoother units, minimizing geometric distortion.
• Prediction Error Expansion (PEE) technique guided by dual sorting for reversible data embedding.

Main Results:

• The proposed method demonstrates a superior balance between high embedding capacity and low geometric distortion.
• Achieves better performance compared to existing state-of-the-art spatial-domain RDH methods for 3D mesh models.

Conclusions:

• The novel RDH method effectively enhances embedding capacity and minimizes geometric distortion in 3D mesh models.
• This research contributes a significant advancement in the field of data hiding for 3D digital media.