Watermarking via optimization algorithms for quantizing randomized semi-global image statistics

We introduce a novel approach for blind and semi-blind watermarking and apply it to images. We derive randomized robust semi-global statistics of images in a suitable transform domain (wavelets in case of images) and quantize them in order to embed the watermark. Quantization is effectively carried out by embedding to the host a computed sequence, which is obtained by solving an optimization problem whose parameters are known to the information hider but unknown to the attacker. An essential emphasis of the proposed method is randomization, which is crucial for security and robustness against arbitrary quality-preserving attacks. We formally show that malicious optimal estimation attacks that are specifically derived for our algorithm are ineffective in practice. Furthermore, we experimentally demonstrate that our watermarking method survives many generic benchmark attacks for a large number of images.