Watermarking via optimization algorithms for quantizing randomized image characteristics

We introduce a novel approach for blind and semi-blind watermarking and apply it to images. We derive randomized robust semi-global features of images in a suitable transform domain (wavelets in case of images) and quantize them in order to embed the watermark. Quantization is carried out by embedding to the host a sequence computed by solving an optimization problem whose parameters are known to the information hider, but unknown to the attacker. We experimentally identify some conditions (our randomization is aimed at achieving them) satisfied by our parameters, which formally and experimentally imply the robustness of our algorithm against malicious optimal estimation attacks. Furthermore, we experimentally show the robustness of our algorithm against many generic benchmark attacks for a large number of images.