Leveraging Data Geometry to Mitigate CSM in Steganalysis
Rony Abecidan Vincent Itier Jérémie Boulanger Patrick Bas Tomáš Pevný
In operational scenarios, steganographers use sets of covers from various
sensors and processing pipelines that differ significantly from those used by
researchers to train steganalysis models. This leads to an inevitable
performance gap when dealing with out-of-distribution covers, commonly referred
to as Cover Source Mismatch (CSM). In this study, we consider the scenario
where test images are processed using the same pipeline. However, knowledge
regarding both the labels and the balance between cover and stego is missing.
Our objective is to identify a training dataset that allows for maximum
generalization to our target. By exploring a grid of processing pipelines
fostering CSM, we discovered a geometrical metric based on the chordal distance
between subspaces spanned by DCTr features, that exhibits high correlation with
operational regret while being not affected by the cover-stego balance. Our
contribution lies in the development of a strategy that enables the selection
or derivation of customized training datasets, enhancing the overall
generalization performance for a given target. Experimental validation
highlights that our geometry-based optimization strategy outperforms
traditional atomistic methods given reasonable assumptions. Additional
resources are available at
github.com/RonyAbecidan/LeveragingGeometrytoMitigateCSM.