  Steganography and steganalysis are two important branches of the information
hiding field of research. Steganography methods consist in hiding information
in such a way that the secret message is undetectable for the uninitiated.
Steganalyzis encompasses all the techniques that attempt to detect the presence
of such hidden information. This latter is usually designed by making
classifiers able to separate innocent images from steganographied ones
according to their differences on well-selected features. We wonder, in this
article whether it is possible to construct a kind of universal steganalyzer
without any knowledge regarding the steganographier side. The effects on the
classification score of a modification of either parameters or methods between
the learning and testing stages are then evaluated, while the possibility to
improve the separation score by merging many methods during learning stage is
deeper investigated.
