Ensemble Classifier~\cite{DBLP:journals/tifs/KodovskyFH12} based steganalyser.
Its particularization to spatial domain is
considered as state of the art steganalysers.
-\JFC{Features that are embedded into this steganalysis process
+Features that are embedded into this steganalysis process
are CCPEV and SPAM features as described
in~\cite{DBLP:dblp_conf/mediaforensics/KodovskyPF10}.
They are extracted from the
-set of cover images and the set of training images.}
+set of cover images and the set of training images.
Next a small
set of weak classifiers is randomly built,
each one working on a subspace of all the features.
providing an efficient steganography approach in a lightweight manner
for small payload.
-\RC{In Figure~\ref{fig:error},
+In Figure~\ref{fig:error},
Ensemble Classifier has been used with all the previous
steganographic schemes with 4 different payloads.
It can be observed that face to high values of payload,
\begin{center}
\includegraphics[scale=0.5]{error}
\end{center}
-\caption{Testing error obtained by Ensemble classifier with
+\caption{Testing errors obtained by Ensemble classifier with
WOW/UNIWARD, HUGO, and STABYLO w.r.t. payload.}
\label{fig:error}
\end{figure}
-}
