+\subsection{Adaptive Embedding Rate}
+
+Two strategies have been developed in our scheme with respect to the rate of
+embedding which is either \emph{ adaptive} or \emph{fixed}.
+
+In the former the embedding rate depends on the number of edge pixels.
+The higher it is, the larger is the message length that can be considered.
+Practically, a set of edge pixels is computed according to the
+Canny algorithm with high threshold.
+The message length is thus defined to be the half of this set cardinality.
+The rate between available bits and bit message length is then more than two.This constraint is indeed induced by the fact that the efficiency
+of the STC algorithm is unsatisfactory under that threshold.
+
+
+In the latter, the embedding rate is defined as a percentage between the
+number of the modified pixels and the length of the bit message.
+This is the classical approach adopted in steganography.
+Practically, the Canny algorithm generates a
+a set of edge pixels with threshold that is decreasing until its cardinality
+is sufficient. If the set cardinality is more than twice larger than the
+bit message length an STC step is again applied.
+Otherwise, pixels are randomly chosen from the set of pixels to build the
+subset with a given size. The BBS PRNG is again applied there.
+
+
+
\subsection{Image Quality}
The visual quality of the STABYLO scheme is evaluated in this section.
-Four metrics are computed in these experiments :
+Four metrics are computed in these experiments:
the Peak Signal to Noise Ratio (PSNR),
-the PSNR-HVS-M familly~\cite{PSECAL07,psnrhvsm11} ,
+the PSNR-HVS-M family~\cite{PSECAL07,psnrhvsm11} ,
the BIQI~\cite{MB10,biqi11} and
-the weigthed PSNR (wPSNR)~\cite{DBLP:conf/ih/PereiraVMMP01}.
+the weighted PSNR (wPSNR)~\cite{DBLP:conf/ih/PereiraVMMP01}.
The first one is widely used but does not take into
account Human Visual System (HVS).
The other last ones have been designed to tackle this problem.
\begin{center}
\begin{tabular}{|c|c|c|}
\hline
-Embedding rate & Adaptive
-10 \% & \\
+Embedding rate & Adaptive & 10 \% \\
\hline
-PSNR & & \\
+PSNR & 66.55 & 61.86 \\
\hline
PSNR-HVS-M & 78.6 & 72.9 \\
\hline
\hline
\end{tabular}
\end{center}
-\caption{Quality measeures of our steganography approach\label{table:quality}}
+\caption{Quality measures of our steganography approach\label{table:quality}}
\end{table}
-Compare to the Edge Adpative scheme detailed in~\cite{Luo:2010:EAI:1824719.1824720}, our both wPSNR and PSNR values are always higher than their ones.
-
-\JFC{comparer aux autres approaches}
-
+Let us compare the STABYLO approach with other edge based steganography
+schemes with respect to the image quality.
+Fist off all, wPSNR and PSNR of the Edge Adaptive
+scheme detailed in~\cite{Luo:2010:EAI:1824719.1824720} are lower than ours.
+Next both the approaches~\cite{DBLP:journals/eswa/ChenCL10,Chang20101286}
+focus on increasing the payload while the PSNR is acceptable, but do not
+give quality metrics for fixed embedding rate from a large base of images.
+Our approach outperforms the former thanks to the introduction of the STC
+algorithm.
\subsection{Steganalysis}
\begin{table}
\begin{center}
\begin{tabular}{|c|c|c|c|}
-Shemes & \multicolumn{2}{|c|}{STABYLO} & HUGO\\
+\hline
+Schemes & \multicolumn{2}{|c|}{STABYLO} & HUGO\\
\hline
Embedding rate & Adaptive & 10 \% & 10 \%\\
\hline
AUMP & 0.39 & 0.22 & 0.50 \\
\hline
-Ensemble Classifier & & & \\
+Ensemble Classifier & 0.47 & 0.35 & 0.48 \\
\hline
\end{tabular}
\end{table}
+Results show that our approach is more easily detectable than HUGO which is
+is the more secure steganography tool, as far we know. However due to its
+huge number of features integration, it is not lightweight.
+
