High-capacity reversible data hiding in encrypted 3D mesh models based on multi-MSB prediction

Reversible data hiding in encrypted domain (RDH-ED) technology performs content encryption to guarantee the privacy of the original media and hide additional data for multimedia management or access control. However, for three dimensional (3D) model, it is still challenging to improve data embedding capacity under the premise of ensuring reversibility. This paper proposes an improvement RDH-ED method for 3D models. Firstly, the vertices are classified into “embedded set” and “prediction set” based on the odd-even property of indices. Multiple most significant bit (multi-MSB) of each vertex is predicted adaptively and marked in the original model to reserve room for data embedding. To further free up the room, the auxiliary information used for data extraction and model recovery can be compressed by entropy coding. Finally, the correlation between two sets is used to recover vertices that were modified due to data embedding. Experimental results show the proposed method significantly improves the embedding capacity compared with state-of-the-art methods.