\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}
\JFC{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
+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,
\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.
-
-\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.\\
+providing an efficient steganography approach in a lightweight manner
+for small payload.
+
+\RC{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{Error obtained by Ensemble classifier with WOW/UNIWARD, HUGO, and STABYLO and different paylaods.}
+\caption{Testing error obtained by Ensemble classifier with
+WOW/UNIWARD, HUGO, and STABYLO w.r.t. payload.}
\label{fig:error}
\end{figure}
}
