Joint iterative decoding and estimation for side-informed data hiding

We present a previously unavailable study on a general procedure for joint iterative decoding and estimation of attack parameters in side-informed data hiding. This type of approach, which exploits iteratively decodable codes for channel identification purposes, has recently become a relevant research trend in many digital communications problems. An advantage is that estimation pilots are not strictly required, thus affording in principle the implementation of blind methods that are able to work close to the theoretically maximum achievable rate. Such a target naturally requires the use of both near-optimum side-informed data hiding methods (e.g., DC-DM) and near-optimum iteratively decodable channel codes (e.g., turbo codes). The attack channels considered in this study are additive independent random noise, amplitude scaling, and a particular case of fine desynchronization of the sampling grid, whose parameters are estimated by maximum likelihood at the decoder. The complexity of this task is tackled by means of the Expectation-Maximization (EM) algorithm, relying on the use of a priori probabilities produced by the iterative decoding process.