A self-similarity based adaptive steganography for 3D point cloud models

This paper presents a new adaptive, high-capacity steganography for 3D point cloud models using self-similarity segmentation. Every embedding vertex of the model can adaptively embed variable σ (σ ≥4) bits using an adaptive self-similarity position matching procedure with low distortion which uses normal direction of vertexes to estimate the embedding capacity of every vertex with respect to human visual system. The new scheme segments the 3D point cloud model to patches using self-similarity measures, every message point in the similar message patches which has the point-to-point correspondence with a certain reference point in the reference patch can adaptively embed at least four bits by shifting the message point from current point to the corresponding embedding position using space subdivision. Experimental results show that the proposed scheme is adaptive, has high capacity and low distortion.