Taking advantage of source correlation in forensic analysis

In a wide range of practical multimedia scenarios several correlated contents are available. The aim of this work is to quantify the gain that can be achieved in forensic applications by jointly considering those contents, instead of analyzing them separately. The used tool is the Kullback-Leibler Divergence between the distributions corresponding to different operators; the Maximum Likelihood estimator of the applied operator is also obtained, in order to illustrate how the correlation is exploited for estimation. Our detailed analysis is constrained to the Gaussian case (both for the input signal distribution and the processing randomness) and linear operators. Several practical scenarios are studied, and the relationships between the derived results are established. Finally, the links with Distributed Source Coding are highlighted.