Many different hypotheses may be chosen for modeling a
steganography/steganalysis problem. In this paper, we look closer into the
case in which Eve, the steganalyst, has partial or erroneous knowledge of
the cover distribution. More precisely we suppose that Eve knows the
algorithms and the payload size that has been used by Alice, the
steganographer, but she ignores the images distribution. In this
source-cover mismatch scenario, we demonstrate that an Ensemble Classifier
with Features Selection (EC-FS) allows the steganalyst to obtain the best
state-of-the-art performances, while requiring 100 times smaller training
database compared to the previous state-of-the art approach. Moreover, we
propose the islet approach in order to increase the classification
performances.