  3D mesh steganographic algorithms based on geometric modification are
vulnerable to 3D steganalyzers. In this paper, we propose a highly adaptive 3D
mesh steganography based on feature-preserving distortion (FPD), which
guarantees high embedding capacity while effectively resisting 3D steganalysis.
Specifically, we first transform vertex coordinates into integers and derive
bitplanes from them to construct the embedding domain. To better measure the
mesh distortion caused by message embedding, we propose FPD based on the most
effective sub-features of the state-of-the-art steganalytic feature set. By
improving and minimizing FPD, we can efficiently calculate the optimal
vertex-changing distribution and simultaneously preserve mesh features, such as
steganalytic and geometric features, to a certain extent. By virtue of the
optimal distribution, we adopt the Q-layered syndrome trellis coding (STC) for
practical message embedding. However, when Q varies, calculating bit
modification probability (BMP) in each layer of Q-layered will be cumbersome.
Hence, we contrapuntally design a universal and automatic BMP calculation
approach. Extensive experimental results demonstrate that the proposed
algorithm outperforms most state-of-the-art 3D mesh steganographic algorithms
in terms of resisting 3D steganalysis.
