A Benchmark Tool for Digital Watermarking

A Benchmark Tool for Digital Watermarking

Keiichi Iwamura (Tokyo University of Science, Japan)
DOI: 10.4018/978-1-4666-2217-3.ch015
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Abstract

This chapter presents an overview of benchmark tools for digital watermarking and describes a new benchmark tool that supports various attacks and has a graphical user interface. Digital watermarks are used to prevent unauthorized use of digital content such as illegal copying, unauthorized distribution, and falsification. Benchmark tools are required to measure the strength of digital watermarks. Stirmark and JEWELS are well-known benchmark tools. However, the functionality of existing tools is insufficient because they lack evaluation functions for multiple image attacks. In addition, users need to memorize each attack command and check results on another viewer because almost all the existing tools are implemented as command-line-based software without image viewers. Therefore, the authors classify attacks on digital watermarks and develop a new benchmark tool that includes attacking functions using multiple as well as single images. In addition, the tool has a graphical user interface that makes it easy to perform combinations of two or more attacks.
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Attacks On Digital Watermarking

Attacks on digital watermarking involve image processing that transforms watermarked information and prevents it from being read. We classify these attacks into two groups.

Single-Image Attacks

A single-image attack is applied to a single image containing a digital watermark. Single-image attacks can employ image-processing operations such as noise addition, JPEG compression, trimming, and scaling. In general, because these operations are frequently performed on images, digital watermarking needs to be robust against these attacks. Some attacks are mounted in many existing image-editing tools, and these are considered to be innocent attacks.

However, malicious attacks intentionally change images to make it impossible to extract the watermark. For example, attacks that create small geometrical distortions in images are well known (Petitcolas, 1998). These attacks warp an image by multiple applications of minute, local rotations, and scaling. In addition, the possibility of Frequency Mode Laplacian Removal (FMLR) attacks has been discovered (Barnett, 1998). These attacks remove watermarks by using Laplacian convolution masks. In general, these single-image attacks degrade image quality.

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