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Proceedings Paper

Digital image watermarking using visual models
Author(s): Christine I. Podilchuk; Wenjun Zeng
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Paper Abstract

The huge success of the Internet permits the transmission and wide distribution and access of electronic data in an effortless manner. Content providers are faced with the challenge of how to protect their electronic data. This problem has generated a flurry of recent research activity in the area of digital watermarking of electronic content for copyright protection. Unlike the traditional visible watermark found on paper, the challenge here is to introduce a digital watermark that does not alter the perceived quality of the electronic content while being extremely robust to attack. For instance, in the case of image data, editing the picture or illegal tampering should not destroy or alter the watermark. Equally important, the watermark should not alter the perceived visual quality of the image. From a signal processing viewpoint, the two basic requirements for an effective watermarking scheme, robustness and transparency, conflict with each other. We propose a watermarking technique for digital images that is based on utilizing visual models which have been developed in the context of image compression. Specifically, we propose a watermarking scheme where visual models are used to determine image dependent modulation masks for watermark insertion. In other words, for each image we can determine the maximum amount of watermark signal that each portion of the image can tolerate without affecting the visual quality of the image. This allow us to provide the maximum strength watermark which in turn, is extremely robust to common image processing and editing such as JPEG compression, rescaling, and cropping. We have watermarking results in a DCT framework as well as a wavelet framework. The DCT framework allows the direct insertion of watermarks to JPEG -- compressed data whereas the wavelet based scheme provides a framework where we can take advantage of both a local and global approach. Our scheme is shown to provide dramatic improvement over the current state-of-the-art both in terms of transparency and robustness.

Paper Details

Date Published: 3 June 1997
PDF: 12 pages
Proc. SPIE 3016, Human Vision and Electronic Imaging II, (3 June 1997); doi: 10.1117/12.274503
Show Author Affiliations
Christine I. Podilchuk, Lucent Technologies Bell Labs. (United States)
Wenjun Zeng, Princeton Univ. (United States)


Published in SPIE Proceedings Vol. 3016:
Human Vision and Electronic Imaging II
Bernice E. Rogowitz; Thrasyvoulos N. Pappas, Editor(s)

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