New Spectral Decomposition for 3D Polygonal Meshes and its Application for Watermarking

This paper present a generalization of a data analysis technique called a singular spectrum analysis (SSA). The original SSA is a tool for analyzing one-dimensional data such as time series, whereas our generalization is suitable for multi-dimensional data such as 3D polygonal meshes. One of applications of the proposed generalization are also shown. The application of the generalized SSA is a new robust watermarking method that adds a watermark to a 3D polygonal mesh. Watermarks embedded by our method are resistant to similarity transformations and random noises. Our method has the advantage in that it requires smaller calculation cost than other methods with nearly equal performance.