A high-capacity data-hiding approach for polygonal meshes using maximum expected level tree

This paper presents a high-capacity data-hiding approach for polygonal meshes using maximum expected level tree based on a message probability model. While embedding/extracting a bitstring for a primitive, a maximum expected level tree is built level-by-level for all remaining primitives arranged in a reference order. At each level, the number of leaf nodes in one subtree is determined based on the occurrence probability of the interested event for the next message bit from the proposed message probability model, such that the traversal path from the root to the embedding/extracting primitive (leaf node) is as long as possible. Moreover, the traversal path represents the embedded/extracted message. Given an embedding message, the 0-bit and 1-bit run-length histograms on which the proposed message probability model is based are computed initially and updated after each embedding/extracting step. When extracting, the histograms are extracted first from the stego model and then used to extract the message from each primitive.The capacity of our approach depends on the run-length histograms of a given embedding message. The experimental results show that our capacity is larger than that of previous methods, the improvements in capacity for text-based, binary-based, and compressed-based message types are approximately 18.21%, 44.79%, and 14.86%, respectively. Moreover, as a permutation steganography approach, the stego model is distortion-free, and the embedded message is imperceptible and robust to geometric affine transformations.

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► A MEL tree is constructed based on the proposed message probability model.
► The tree traversal from root to a leaf node is used to encode/decode the message.
► The capacity depends on the run-length histograms of a given message.
► The approach is distortion free and robust to geometric affine transformations.