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+\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: 
+the Peak Signal to Noise Ratio (PSNR), 
+the PSNR-HVS-M family~\cite{PSECAL07,psnrhvsm11} , 
+the BIQI~\cite{MB10,biqi11} and 
+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{table}
+\begin{center}
+\begin{tabular}{|c|c|c|}
+\hline
+Embedding rate &  Adaptive & 10 \%  \\
+\hline
+PSNR &  66.55    & 61.86     \\
+\hline
+PSNR-HVS-M & 78.6  & 72.9 \\
+\hline
+BIQI & 28.3 & 28.4 \\
+\hline
+wPSNR & 86.43& 77.47 \\
+\hline
+\end{tabular}
+\end{center}
+\caption{Quality measures of our steganography approach\label{table:quality}} 
+\end{table}
+
+
+Let us compare the STABYLO approach with other edge based steganography
+schemes with respect to the image quality.
+First of 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}
 
 
-Détailler \cite{Fillatre:2012:ASL:2333143.2333587}
 
-Vainqueur du BOSS challenge~\cite{DBLP:journals/tifs/KodovskyFH12}
+The quality of our approach has been evaluated through the two 
+AUMP~\cite{Fillatre:2012:ASL:2333143.2333587}
+and Ensemble Classifier~\cite{DBLP:journals/tifs/KodovskyFH12} based steganalysers.
+Both aims at detecting hidden bits in grayscale natural images and are 
+considered as the state of the art of steganalysers in spatial domain~\cite{FK12}.
+The former approach is based on a simplified parametric model of natural images.
+Parameters are firstly estimated and a adaptive Asymptotically Uniformly Most Powerful
+(AUMP) test is designed (theoretically and practically) to check whether
+a natural image has stego content or not.  
+In the latter, the authors show that the 
+machine learning step, (which is often
+implemented as support vector machine)
+can be a favourably executed thanks to an Ensemble Classifiers.
+
+
+
+\begin{table}
+\begin{center}
+\begin{tabular}{|c|c|c|c|}
+\hline
+Schemes & \multicolumn{2}{|c|}{STABYLO} & HUGO\\
+\hline
+Embedding rate &  Adaptive & 10 \% &  10 \%\\
+\hline
+AUMP & 0.39  & 0.22     &  0.50     \\
+\hline
+Ensemble Classifier & 0.47   & 0.35     & 0.48     \\
+
+\hline
+\end{tabular}
+\end{center}
+\caption{Steganalysing STABYLO\label{table:steganalyse}} 
+\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.   
+