Color Picture Watermarking Correlating Two Constituent Planes for Immunity to Random Geometric Distortion


IEICE TRANSACTIONS on Information and Systems   Vol.E87-D    No.9    pp.2239-2252
Publication Date: 2004/09/01
Online ISSN: 
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
watermarking,  digital picture,  survivability,  StirMark attack,  

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Digital watermarks on pictures must have the ability to survive various image processing operations while not causing degradation of picture quality. Random geometric distortion is one of the most difficult kinds of image processing for a watermark to survive, and this problem has become a central issue in watermarking research. Previous methods for dealing with random geometric distortion have been based on searches, special watermark patterns, learning, or additional data such as original pictures. Their use, however, is accompanied by large computational overhead or by operational inconvenience. This paper therefore proposes a method based on embedding watermark patterns in two of the three color planes constituting a color picture so that these two planes have a specific covariance. The detection of the embedded information is based on the covariance between these two planes. Random geometric distortion distorts all the constituent color planes of a picture in the same way and thus does not affect the covariance between any two. The covariance-based detection is therefore immune to the distortion. The paper clarifies that detection error would occur whenever the inherent covariance (the covariance in the original picture) overrides the covariance made by watermarking. The two constituent planes having the minimum inherent covariance are therefore selected and their inherent covariance is reduced by shifting one of them and using a noise-reduction preprocess. Experimental evaluations using StirMark confirmed that 64 bits embedded in 256256-pixel pictures can be correctly detected without using searches, special patterns, learning, or additional data.