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ICIP
2008
IEEE

Principal Component Analysis of spectral coefficients for mesh watermarking

9 years 4 months ago
Principal Component Analysis of spectral coefficients for mesh watermarking
This paper proposes a new robust 3-D object blind watermarking method using constraints in the spectral domain. Mesh watermarking in spectral domain has the property of spreading the information in unpredictable ways, thus increasing the security of the watermark. In the proposed method, firstly, the Laplacian matrix of the graphical object mesh is eigen-decomposed. The coefficients corresponding to the higher spectra are split into sets and each set is used for embedding one bit. A bit of 1 is embedded by introducing an asymmetry in the 3-D distribution of the spectral coefficients from the given set, while the distribution symmetry is enforced in the case when embedding a bit of 0. The Principal Component Analysis (PCA) is used for embedding the constraints in the spectral domain by ensuring a minimal distortion. Comparison results are provided for various attacks.
Ming Luo, Adrian G. Bors
Added 20 Oct 2009
Updated 27 Oct 2009
Type Conference
Year 2008
Where ICIP
Authors Ming Luo, Adrian G. Bors
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