Abstract
This study adopts a triangle subdivision scheme to achieve reversible data embedding. The secret message is embedded into the newly added vertices. The topology of added vertex is constructed by connecting it with the vertices of located triangle. For further raising the total embedding capacity, a recursive subdivision mechanism, terminated by a given criterion, is employed. Finally, a principal component analysis can make the stego model against similarity transformation and vertex/triangle reordering attacks. Our proposed algorithm can provide a high and adjustable embedding capacity with reversibility. The experimental results demonstrate the feasibility of our proposed algorithm.
Highlights
Reversible data hiding algorithms [1,2,3,4,5,6,7,8,9,10,11,12] can recover the marked media to original one after the secret message is correctly extracted
We show the file size for each test model, stored in PLY file format, for later comparing the one of each stego model
This study proposes a novel reversible data hiding algorithm for 3D triangular meshes based on recursive subdivision
Summary
Reversible data hiding algorithms [1,2,3,4,5,6,7,8,9,10,11,12] can recover the marked media to original one after the secret message is correctly extracted. Jhou et al [4] applied histogram modification on 3D models They first calculate the gravity center of the input model and the embedding order for each vertex is determined based on its distance to the center. Chuang et al [1] employed the Helmert transformation to raise the robustness against similarity transformation attacks Their algorithm performs data embedding on the normalized distance between each vertex and the model center. Wu and Dugelay [9] proposed a reversible data hiding algorithm based on difference expansion. They first predict a vertex position by calculating the center of its traversed neighbors. The secret message is embedded by expanding the difference between the predicted and the original coordinate values. The secret message is embedded into the difference between every two adjacent vertices based on difference expansion and difference shifting
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