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A Color Image Watermarking Based on Tensor Analysis
Author(s) -
Haiyong Xu,
Gangyi Jiang,
Mei Yu,
Ting Luo
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2866287
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Since most of the color image watermarking methods embed the watermark information in each channel or one channel of a color image, the redundant information of the color image cannot be sufficiently utilized, resulting in the poor ability to resist attacks. In this paper, a novel blind color image watermarking method based on the tensor domain is proposed, and it takes efficient account of the overall characteristics of color images and spreads the watermark information into three channels of color images based on tensor decomposition. Exploring the new tensor domain to embed and extract watermark information and the theoretical proof of the optimal embedding position of the core tensor are the major technical contributions of this paper. To be more specific, first, the RGB channels of the color image as a tensor are considered, and the tensor decomposition is used to obtain the core tensor. Then, based on the theoretical proof, the optimal embedding position of the core tensor is obtained, and the watermark information is embedded into the core tensor. Finally, the watermark information is spread to the three channels of the color image through the inverse tensor decomposition. The experimental results show that the proposed method has better invisibility and stronger robustness for the most common attacks.

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