Ensemble Classifier~\cite{DBLP:journals/tifs/KodovskyFH12} based steganalyser.
Its particularization to spatial domain is
considered as state of the art steganalysers.
-Firstly, a space
-of 686 co-occurrence and Markov features is extracted from the
-set of cover images and the set of training images. Next a small
+\JFC{Features that are embedded into this steganalysis process
+are CCPEV and SPAM features as described
+in~\cite{DBLP:dblp_conf/mediaforensics/KodovskyPF10}.
+These latter are extracted from the
+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.
The final classifier is constructed by a majority voting
All these numerical experiments confirm
the objective presented in the motivations:
providing an efficient steganography approach in a lightweight manner.
+
+\RC{In Figure~\ref{fig:error}, Ensemble Classifier has been used with all the previsou steganalizers with 3 different payloads. It can be observed that with important payload, STABYLO is not efficient, but as mentionned its complexity is far more simple compared to other tools.\\
+\begin{figure}
+\begin{center}
+\includegraphics[scale=0.5]{error}
+\end{center}
+\caption{Error obtained by Ensemble classifier with WOW/UNIWARD, HUGO, and STABYLO and different paylaods.}
+\label{fig:error}
+\end{figure}
+}
