Optimizing pooling function for pooled steganalysis

Abstract

Pooled steganalysis combines evidence from multiple objects to achieve higher accuracy in detecting hidden messages at the expense of granularity, as the decision is provided on the set of objects instead of a single one. Although it has been introduced almost decade ago, very little work has been done since then. This work builds upon recent advances in machine learning to show, how an optimal function combining outputs of a single object detector on a set of objects can be learned. Although experiments demonstrate that learned combining functions are superior to the prior art, more importantly they reveal many interesting phenomenons and points to direction of further research.