Abstract
When a steganalysis detector trained on one cover source is applied to images from a different source, generally
the detection error increases due to the mismatch between both sources. In steganography, this situation is
recognized as the so-called cover source mismatch (CSM). The drop in detection accuracy depends on many
factors, including the properties of both sources, the detector construction, the feature space used to represent
the covers, and the steganographic algorithm. Although well recognized as the single most important factor
negatively affecting the performance of steganalyzers in practice, the CSM received surprisingly little attention
from researchers. One of the reasons for this is the diversity with which the CSM can manifest. On a series of
experiments in the spatial and JPEG domains, we refute some of the common misconceptions that the severity
of the CSM is tied to the feature dimensionality or their “fragility.” The CSM impact on detection appears too
difficult to predict due to the effect of complex dependencies among the features. We also investigate ways to
mitigate the negative effect of the CSM using simple measures, such as by enlarging the diversity of the training
set (training on a mixture of sources) and by employing a bank of detectors trained on multiple different sources
and testing on a detector trained on the closest source.
