Gaussian Model for 3D Mesh Steganography
Jiahao Zhu, Yushu Zhang, Xinpeng Zhang, Xiaochun Cao
Abstract
Currently,mainstream 3D steganographic algorithms embed data through geometric modifications. To enhance the anti-steganalysis ability of such algorithms,we propose an additive Gaussian noise model for 3D mesh steganography. Our work starts with a theoretical analysis for correlations of vertex coordinate components. Based on this analysis,the whole embedding operation is decomposed into three independent tasks that target three vertex coordinate components,respectively. To obtain vertex-changing probabilities,we construct a payload-limited sender (PLS) problem aimed at minimizing the Kullback-Leibler divergence between the cover and stego mesh distributions for a given payload. Next,vertex coordinates are quantified so that the PLS problem can be solved in practice. Finally,a ternary embedding scheme is taken as a typical steganographic case. Although the proposed Gaussian model is simple and idealized,the experimental results demonstrate that our algorithm possesses the adaptivity and can achieve better anti-steganalysis performance at low payloads than most modern high-capacity 3D mesh steganography based on the geometric modification.