Paper
20 June 2003 Optimizing watermark robustness with respect to a perceptual distortion constraint
Justin Picard, Arnaud Robert
Author Affiliations +
Proceedings Volume 5020, Security and Watermarking of Multimedia Contents V; (2003) https://doi.org/10.1117/12.477337
Event: Electronic Imaging 2003, 2003, Santa Clara, CA, United States
Abstract
Most watermarking applications require that the embedded watermark be imperceptible. Accordingly, perceptual masking models that identify unperceived regions of the signal were adapted in a straightforward manner to watermarking. The derived mask -- or slack -- is often interpreted as the maximal allowed distortion within a given region of the signal; it is used in many watermarking embedding methods to shape a white spectrum message, in the relevant transform domain (space, frequency). Such a usage of the mask is intuitively satisfying since imperceptibility is indeed guaranteed; yet, it discards any guarantee of robustness to attacks -- another fundamental, necessary property of watermarks. The trade-off between fidelity and robustness has been little addressed so far due in great part to the absence of an accurate measure of perceptual distortion. In this paper we study this trade-off using Watson's measure of perceptual distance between two images as the measure of fidelity. Based on a constrained perceptual distance, the embedder must maximize the watermark's robustness while assuming a knowledgeable attacker will attempt to remove the watermark. Solving this problem leads to an optimized watermark strength for each location of the content.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Justin Picard and Arnaud Robert "Optimizing watermark robustness with respect to a perceptual distortion constraint", Proc. SPIE 5020, Security and Watermarking of Multimedia Contents V, (20 June 2003); https://doi.org/10.1117/12.477337
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KEYWORDS
Digital watermarking

Distortion

Distance measurement

Linear filtering

Signal to noise ratio

Sensors

Signal detection

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