  Within an operational framework, covers used by a steganographer are likely
to come from different sensors and different processing pipelines than the ones
used by researchers for training their steganalysis models. Thus, a performance
gap is unavoidable when it comes to out-of-distributions covers, an extremely
frequent scenario called Cover Source Mismatch (CSM). Here, we explore a grid
of processing pipelines to study the origins of CSM, to better understand it,
and to better tackle it. A set-covering greedy algorithm is used to select
representative pipelines minimizing the maximum regret between the
representative and the pipelines within the set. Our main contribution is a
methodology for generating relevant bases able to tackle operational CSM.
Experimental validation highlights that, for a given number of training
samples, our set covering selection is a better strategy than selecting random
pipelines or using all the available pipelines. Our analysis also shows that
parameters as denoising, sharpening, and downsampling are very important to
foster diversity. Finally, different benchmarks for classical and wild
databases show the good generalization property of the extracted databases.
Additional resources are available at
github.com/RonyAbecidan/HolisticSteganalysisWithSetCovering.
