Complexity regularized video watermarking via quantization of pseudo-random semi-global linear statistics

In this paper, we propose a semi-blind video watermarking scheme for the verification problem, where mark embedding is carried out by designing a complexity-adaptive watermark signal via solving a constrained optimization problem. By adding the resulting watermark to the unmarked host, we effectively quantize pseudorandom linear statistics of the host in the wavelet domain using a secret codebook. We introduce a stochastic video model and exploit it during the design. In particular, we use the proposed model to generate a “complexity map”, which is then employed in solving the underlying optimization problem to “regularize” the watermark. Consequently, the resulting watermark is locally adapted to the statistical complexity of the signal at a coefficient level. Regularization is achieved by solving the underlying optimization problem using an iterative algorithm. We experimentally validate the complexity-adaptive structure of the resulting scheme, while maintaining robustness against numerous attacks, such as low bit rate video compression, mild geometric modifications, etc. This paper can be viewed as a continuation of our previous work [1].