  Steganalysis is a collection of techniques used to detect whether secret
information is embedded in a carrier using steganography. Most of the existing
steganalytic methods are based on machine learning, which typically requires
training a classifier with "laboratory" data. However, applying
machine-learning classification to a new source of data is challenging, since
there is typically a mismatch between the training and the testing sets. In
addition, other sources of uncertainty affect the steganlytic process,
including the mismatch between the targeted and the true steganographic
algorithms, unknown parameters -- such as the message length -- and even having
a mixture of several algorithms and parameters, which would constitute a
realistic scenario. This paper presents subsequent embedding as a valuable
strategy that can be incorporated into modern steganalysis. Although this
solution has been applied in previous works, a theoretical basis for this
strategy was missing. Here, we cover this research gap by introducing the
"directionality" property of features with respect to data embedding. Once this
strategy is sustained by a consistent theoretical framework, new practical
applications are also described and tested against standard steganography,
moving steganalysis closer to real-world conditions.
