Robust Zero Watermarking for Still and Similar Images Using a Learning Based Contour Detection

Abstract

Digital watermarking is an efficacious technique to protect the copyright and ownership of digital information. Traditional image watermarking algorithms embed a logo in the image that reduces its visual quality. A new approach in watermarking called zero watermarking doesn’t need to embed a logo in the image. In this algorithm we find a feature from the main image and combine it with a logo to obtain a key. This key is securely kept by a trusted authority. In this paper we show that we can increase the robustness of digital zero watermarking by a new counter detection method in comparison to Canny Edge detection and morphological dilatation that is mostly used by related works. Experimental results demonstrate that our proposed scheme is robust against common geometric and non-geometric attacks including blurring, JPEG compression, noise addition, Sharpening, scaling, rotation, and cropping. The main advantage of the proposed method is its ability to distinguishable key for images taken from the same scene with small angular rotation and minor displacement.