Evaluation of Feature Extraction Techniques for Robust Watermarking

Abstract

This paper addresses feature extraction techniques for robust watermarking. Geometric distortion attacks desynchronize the location of the inserted watermark and hence prevent watermark detection. Watermark synchronization, which is a process of finding the location for watermark insertion and detection, is crucial to design robust watermarking. One solution is to use image features. This paper reviews feature extraction techniques that have been used in featurebased watermarking: the Harris corner detector and the Mexican Hat wavelet scale interaction method. We also evaluate the scale-invariant keypoint extractor in comparison with other techniques in aspect of watermarking. After feature extraction, the set of triangles is generated by Delaunay tessellation. These triangles are the location for watermark insertion and detection. Redetection ratio of triangles is evaluated against geometric distortion attacks as well as signal processing attacks. Experimental results show that the scale-invariant keypoint extractor is appropriate for robust watermarking.KeywordsFeature PointImage WatermarkWatermark SchemeFeature Extraction TechniqueRobust WatermarkThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.