Feature extraction for detection of watermarking algorithm

Abstract

The concept of digital watermarking has been developed to solve problems such as illegal duplication and distribution of digital media. In watermarking, the process of removing the watermark from the media is known as an attack. Typically, attacks are carried out using tools such as Stirmark. Some attacks are executed in a targeted manner; in other words, knowing the watermarking algorithm, they directly seek to destroy the watermark in the media. In this type of attack, the damage caused to the media is less extensive than generalized attacks such as Stirmark. Clearly, targeted attacks require prior knowledge about the watermarking algorithm. To the best of our knowledge, algorithm detection in watermarking remains to be investigated. One possible approach is to use staganalysis feature sets; however, we demonstrate that, despite their large number of features, such feature sets do not produce adequate results for watermarking. In this paper, several features are introduced, which can be used in an SVM classifier to allow the detection of the watermarking algorithm. According to implementation results, although the proposed feature set is small, its accuracy is substantially greater than that of the staganalysis feature sets.