Result: Steganography on 3D models using a spatial subdivision technique

Title:
Steganography on 3D models using a spatial subdivision technique
Source:
Advances in computer graphics (24th Computer Graphics International Conference, CGI 2006, Hangzhou, China, June 26-28, 2006)0CGI 2006. :469-476
Publisher Information:
Berlin: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 17 ref 1
Original Material:
INIST-CNRS
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Institute of Computer Science, National Chung-Hsing University, Tawain, Province of China
Department of Information Management, Hsiuping Institute of Technology, Taichung, 412, Tawain, Province of China
ISSN:
0302-9743
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.19104889
Database:
PASCAL Archive

Further Information

This paper proposes a new steganography algorithm for 3D models using a spatial subdivision technique. Our algorithm first decomposes the bounding volume of the cover model into voxels based on a Binary Space Partitioning (BSP) tree. The voxels are then further categorized into eight subspaces, each of which is numbered and represented as three-digit binary characters. In the embedding process, we first traverse the BSP tree, locating a leaf voxel; then we embed every three bits of the payload message into the vertex inside the leaf voxel. This is realized by translating a vertex's current position to the corresponding numbered subspace. This technique is a substitutive blind extraction scheme, where messages embedded can be extracted without the aid of the original cover model. This technique achieves high data capacity, equivalent to at least three times the number of the embedded vertices in the cover model. In addition, the stego model has insignificant visual distortion. Finally, this scheme is robust against similarity transformation attacks.