Skip to Main Content
This paper presents novel techniques for detecting watermarks in images in a known-cover attack framework using natural scene models. Specifically, we consider a class of watermarking algorithms, popularly known as spread spectrum-based techniques. We attempt to classify images as either watermarked or distorted by common signal processing operations like compression, additive noise etc. The basic idea is that the statistical distortion introduced by spread spectrum watermarking is very different from that introduced by other common distortions. Our results are very promising and indicate that this statistical framework is effective in the steganalysis of spread spectrum watermarks.