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.

• Type: Conference
• Year: 2008
• Where: ICIP
• Authors: Ming Luo, Adrian G. Bors
• Keywords: Graphical Object Mesh, ICIP 2008, Image Processing, Object Blind Watermarking, Principal Component Analysis, Spectral Coefficients, Spectral Domain