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: a) watermark estimation; b) 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, e.g., the human auditory system in case of audio watermarks and the human visual system (HVS) 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 summerize 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.