\hline
\end{array}
$$
-\caption{Matrix Generator for $\hat{H}$ in STC}\label{table:matrices:H}
+\caption{Matrix Generator for $\hat{H}$ in STC.}\label{table:matrices:H}
\end{table}
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}.
-These one are extracted from the
-set of cover images and the set of training images.}
+They 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.
\end{tabular}
\end{small}
\end{center}
-\caption{Steganalysing STABYLO\label{table:steganalyse}}
+\caption{Steganalysing STABYLO\label{table:steganalyse}.}
\end{table*}
All these numerical experiments confirm
the objective presented in the motivations:
-providing an efficient steganography approach in a lightweight manner.
+providing an efficient steganography approach in a lightweight manner
+for small payload.
+
+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,
+STABYLO is definitely not secure enough.
+However thanks to an efficient very low-complexity (Fig.\ref{fig:compared}),
+we argue that the user should embed tiny messages in many images
+than a larger message in only one image.
+\begin{figure}
+\begin{center}
+\includegraphics[scale=0.5]{error}
+\end{center}
+\caption{Testing errors obtained by Ensemble classifier with
+WOW/UNIWARD, HUGO, and STABYLO w.r.t. payload.}
+\label{fig:error}
+\end{figure}
