Robust 3D Authentication on Point Clouds with Random Sampling and Sequential Dependency

This study integrates random sampling and sequential dependency to propose a robust 3D authentication algorithm on point clouds. First, the random sampling technique generates lots of virtual points distributed within the bounding volume of the input point cloud. For be robust against the point removal attacks, the proposed algorithm generates the authentication code for the processing point from the spatial relationship between the previous processed point and its corresponding virtual points. Finally, parts of the authentication code are embedded by adjusting the length between the processing point to the center of its corresponding virtual points. The feasibility is demonstrated by extensive experiments with high embedding capacity and low visual distortion. The robustness can be also effectively raised.