Distortion Design for Secure Adaptive 3-D Mesh Steganography

We propose a novel technique for steganography on 3-D meshes so as to resist steganalysis. The majority of existing methods modulate vertex coordinates to embed messages in a nonadaptive way. We take account of complexity of local regions as joint distortion of a triple unit (vertice) and coding method such as syndrome trellis codes to adaptively embed messages, which owns stronger security with respect to existing steganalysis. Key to the distortion is a novel formulation of adaptive steganography, which relies on some effective steganalytic features such as variation of vertex normal. We provide quantitative and qualitative comparisons of our method with several baselines against steganalytic features LFS64, LFS76, and ensemble classifiers, and show that it outperforms the current state of the art. Meanwhile, we proposed an attacking method on steganography proposed by Chao et al. (2009) with a high detection rate.