Watermarking is a potential method for protection of ownership rights on digital audio, image, and video data. Benchmarks are used to evaluate the performance of different watermarking algorithms. For image watermarking, the Stirmark package is the most popular benchmark, and the best current algorithms perform well against it. However, results obtained by the Stirmark benchmark have to be handled carefully since Stirmark does not properly model the watermarking process and consequently is limited in its potential for impairing sophisticated image watermarking schemes. In this context, the goal of this article is threefold. First, we give an overview of the current attacking methods. Second, we describe attacks exploiting knowledge about the statistics of the original data and the embedded watermark. We propose a stochastic formulation of estimation-based attacks. Such attacks consist of two main stages:• Watermark estimation • Exploitation of the estimated watermark to trick watermark detection or create ownership ambiguity The full strength of estimation-based attacks can be achieved by introducing additional noise, where the attacker tries to combine the estimated watermark and the additive noise to impair watermark communication as much as possible while fulfilling a quality constraint on the attacked data. With a sophisticated quality constraint it is also possible to exploit human perception: the human auditory system in case of audio watermarks and the human visual system in case of image and video watermarks. Third, we discuss the current status of image watermarking benchmarks. We briefly present Fabien Petitcolas' Stirmark benchmarking tool [1]. Next, we consider the benchmark proposed by the University of Geneva Vision Group that contains more deliberate attacks. Finally, we summarize the current work of the European Certimark project, whose goal is to accelerate efforts from a number of research groups and companies in order to produce an improved ensemble of benchmarking tools.
Abstract-This paper presents a model for watermarking and some attacks on watermarks. Given the watermarked signal, the so-called Wiener attack performs minimum mean-squared error (MMSE) estimation of the watermark and subtracts the weighted MMSE estimate from the watermarked signal. Under the assumption of a fixed correlation detector, the attack is shown to minimize the expected correlation statistic for the same attack distortion among linear, shift-invariant filtering attacks. It also leads to the idea of energy-efficient watermarking-watermarking that resists MMSE estimation as much as possible-and provides a meaningful way to evaluate robustness. The paper shows that energy-efficient watermarks must satisfy a power-spectrum condition (PSC), which states that the watermark's power spectrum should be directly proportional to the original signal's. PSC-compliant watermarks are proven to be most robust. Experiments with signal models and natural images demonstrate that watermarks that do not closely fulfill the PSC are vulnerable to the Wiener attack, while PSC-compliant watermarks are highly resistant to it. These theoretical and experimental results justify prior heuristic arguments that, for maximum robustness, a watermark should be closely matched to the spectral content of the original signal. The results also discourage the use of watermarks that do not approximately satisfy the PSC.Index Terms-Digital watermarking, robustness, spread spectrum, watermark attacks.
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