Application of Grubbs' test for outliers to the detection of watermarks

Abstract

In an era when the protection of intellectual property rights becomes more and more important, providing robust and efficient watermarking techniques is crucial, both in terms of embedding and detection. In this paper, the authors specifically focus on the latter stage. Most often, the detection consists in the comparison of a fixed and non-adaptive decision threshold to a correlation coefficient. This threshold is usually determined either theoretically or experimentally. Here, it is proposed to apply Grubbs? test, a simple statistical test for outliers, on the correlation data in order to take a binary decision about the presence or the absence of the searched watermark. The proposed technique is applied to three algorithms from the literature: the correlation data generated by the detector is fed to Grubbs' test. The obtained results show that Grubbs' test is efficient, robust and reliable. Above all, it automatically adapts to the searched watermark and can be easily applied to most types of watermarking approaches.