A High-Capacity 3D Steganography Algorithm With Adjustable Distortion

In this paper, we devise a novel steganography algorithm that has a high capacity while still retaining the ability of adjusting the embedding distortion. A shifting strategy is explored to embed the secret data into a given 3-D model effectively. In order to reduce the embedding distortion, we propose a truncated space of data instead of directly working on the critical geometric information from vertices of the cover model. The truncated space confines the distortion of each component of the stego-model within the space that means the embedding distortion could be controlled within a very low threshold. In theory, we can set the length of data truncation to adjust the embedding distortion below a specified level, at the cost of losing certain embedding capacity. Moreover, the embedding capacity is irrelevant to the shape of models, and the quality of the stego-model is mostly dependent on the length of truncation rather than the quantity of embedded secret data. The proposed 3-D steganography method has the capability to control the level of embedding distortion, and at the same time, has a high embedding capacity. Various experiments have demonstrated the flexibility and high performance of our new approach.