Maintaining Picture Quality and Improving Robustness of Color Watermarking by Using Human Vision Models


IEICE TRANSACTIONS on Information and Systems   Vol.E89-D    No.1    pp.256-270
Publication Date: 2006/01/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e89-d.1.256
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Application Information Security
watermarking,  digital picture,  survivability,  StirMark attack,  picture quality,  

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Digital watermarks on pictures are more useful when they are better able to survive image processing operations and when they cause less degradation of picture quality. Random geometric distortion is one of the most difficult kinds of image processing for watermarks to survive because of the difficulty of synchronizing the expected watermark patterns to the watermarks embedded in pictures. This paper proposes three methods to improve a previous method that is not affected by this difficulty but that is insufficient in maintaining picture quality and treating other problems in surviving image processing. The first method determines the watermark strength in L*u*v* space, where human-perceived degradation of picture quality can be measured in terms of Euclidian distance, but embeds and detects watermarks in YUV space, where the detection is more reliable. The second method, based on the knowledge of image quantization, uses the messiness of color planes to hide watermarks. The third method reduces detection noises by preprocessing the watermarked image with orientation-sensitive image filtering, which is especially effective in picture portions where pixel values change drastically. Subjective evaluations have shown that these methods improved the picture quality of the previous method by 0.5 point of the mean evaluation score at the representative example case. On the other hand, the watermark strength of the previous method could be increased by 30% through 60% while keeping the same picture quality. Robustness to image processing has been evaluated for random geometric distortion, JPEG compression, Gaussian noise addition, and median filtering and it was clarified that these methods reduced the detection error ratio to 1/10 through 1/4. These methods can be applied not only to the previous method but also to other types of pixel-domain watermarking such as the Patchwork watermarking method and, with modification, to frequency-domain watermarking.