A Reversible Steganographic Algorithm for Point-Sampled Geometry

This paper proposes a new reversible steganographic algorithm for point-sampled geometry. To the best of our knowledge, our scheme is the first in the literature for recovering the original point-sampled model using little amount of information (two integers and 25 floating points of memory). Our scheme shows high embedding data capacity, being three times the number of points in the models. The scheme first produces three principal axes and to construct a PCA-coordinate system. We then translate the coordinates of the original points to the PCA-coordinate system in order to achieve robustness against translation, rotation, and uniform scaling operations. Second, we sort the points´ coordinates for each axis to yield intervals which are the embedding positions. Finally, we modulate the positions of the points to embed the information and record the modulation information in the model to achieve reversibility. Experimental results verify the feasibility of our scheme.