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Your search keyword '"WANG Peng-cheng"' showing total 17 results
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8. Materials and Design

Author

Sang, Xiao Ming, primary, Wang, Peng Cheng, additional, Ai, Li Qun, additional, Li, Yun Gang, additional, and Bu, Jing Long, additional

Published
2011
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16. Steganography for Three-Dimensional Models.

Author

Nishita, Tomoyuki, Peng, Qunsheng, Seidel, Hans-Peter, Cheng, Yu-Ming, Wang, Chung-Ming, Tsai, Yuan-Yu, Chang, Chung-Hsien, and Wang, Peng-Cheng

Abstract

We present a data hiding algorithm for 3D models. It is based on a substitutive procedure in the spatial domain. We propose a Virtual Multi-Level Embed Procedure to embed information based on shifting the message point by its virtual geometrical property, the order of which is assigned by principal component analysis. We have defined and validated an effective metric of distortion anticipation, which can help us easily anticipate and control the distortion rate. Experimental results show that the proposed technique is efficient and secure, has high capacity and low distortion, and is robust against affine transformations. It provides a reversible method and has proven to be feasible in data hiding. [ABSTRACT FROM AUTHOR]

Published
2006
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17. Steganography on 3D Models Using a Spatial Subdivision Technique.

Author

Nishita, Tomoyuki, Peng, Qunsheng, Seidel, Hans-Peter, Tsai, Yuan-Yu, Wang, Chung-Ming, Cheng, Yu-Ming, Chang, Chung-Hsien, and Wang, Peng-Cheng

Abstract

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. [ABSTRACT FROM AUTHOR]

Published
2006
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