+For whole experiments, a set of 500 images is randomly extracted
+from the database taken from the BOSS contest~\cite{Boss10}.
+In this set, each cover is a $512\times 512$
+grayscale digital image.
+
+
\subsection{Adaptive Embedding Rate}
+Two strategies have been developed in our scheme, depending on the embedding rate that 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 inserted.
+Practically, a set of edge pixels is computed according to the
+Canny algorithm with an high threshold.
+The message length is thus defined to be the half of this set cardinality.
+In this strategy, two methods are thus applied to extract bits that
+are modified. The first one is a direct application of the STC algorithm.
+This method is further referred as \emph{adaptive+STC}.
+The second one randomly chooses the subset of pixels to modify by
+applying the BBS PRNG again. This method is denoted \emph{adaptive+sample}.
+Notice that the rate between
+available bits and bit message length is always equal to 2.
+This constraint is indeed induced by the fact that the efficiency
+of the STC algorithm is unsatisfactory under that threshold.
+On our experiments and with the adaptive scheme,
+the average size of the message that can be embedded is 16445.
+Its corresponds to an average payload of 6.35\%.
+
+
+
+In the latter, the embedding rate is defined as a percentage between the
+number of 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, a STC step is again applied.
+Otherwise, pixels are again randomly chosen with BBS.
+
+
\subsection{Image Quality}
The visual quality of the STABYLO scheme is evaluated in this section.
-Four metrics are computed in these experiments :
+For the sake of completeness, four metrics are computed in these experiments:
the Peak Signal to Noise Ratio (PSNR),
-the PSNR-HVS-M familly~\cite{PSECAL07,psnrhvsm11} ,
-the BIQI~\cite{MB10,biqi11} and
-the weigthed PSNR (wPSNR)~\cite{DBLP:conf/ih/PereiraVMMP01}.
+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.
+account the Human Visual System (HVS).
+The other ones have been designed to tackle this problem.
\begin{table}
\begin{center}
-\begin{tabular}{|c|c|c|}
+\begin{tabular}{|c|c|c||c|c|}
+\hline
+Schemes & \multicolumn{3}{|c|}{STABYLO} & HUGO\\
+\hline
+Embedding & \multicolumn{2}{|c||}{Adaptive} & Fixed & Fixed \\
\hline
-Embedding rate & Adaptive
-10 \% & \\
+Rate & + STC & + sample & 10\% & 10\%\\
\hline
-PSNR & 66.55 & 61.86 \\
+PSNR & 66.55 & 63.48 & 61.86 & 64.65 \\
\hline
-PSNR-HVS-M & 78.6 & 72.9 \\
+PSNR-HVS-M & 78.6 & 75.39 & 72.9 & 76.67\\
\hline
-BIQI & 28.3 & 28.4 \\
+BIQI & 28.3 & 28.28 & 28.4 & 28.28\\
\hline
-wPSNR & 86.43& 77.47 \\
+wPSNR & 86.43& 80.59 & 77.47& 83.03\\
\hline
\end{tabular}
\end{center}
-\caption{Quality measeures of our steganography approach\label{table:quality}}
+\caption{Quality Measures of Steganography Approaches\label{table:quality}}
\end{table}
+Let us give an interpretation of these experiments.
+First of all, the adaptive strategy produces images with lower distortion
+than the one of images resulting from the 10\% fixed strategy.
+Numerical results are indeed always greater for the former strategy than
+for the latter, except for the BIQI metrics where differences are not really relevant.
+These results are not surprising since the adaptive strategy aims at
+embedding messages whose length is decided according to an higher threshold
+into the edge detection.
+Let us focus on the quality of HUGO images: with a given fixed
+embedding rate (10\%),
+HUGO always produces images whose quality is higher than the STABYLO's one.
+However, our approach nevertheless provides better results with the strategy
+\emph{adaptive+STC} in a lightweight manner, as motivated in the introduction.
+
+
+Let us now compare the STABYLO approach with other edge based steganography
+schemes with respect to the image quality.
+First of all, the Edge Adaptive
+scheme detailed in~\cite{Luo:2010:EAI:1824719.1824720}
+executed with a 10\% embedding rate
+has the same PSNR but a lower wPSNR than ours:
+these two metrics are respectively equal to 61.9 and 68.9.
+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.
-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}
\subsection{Steganalysis}
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.
+Parameters are firstly estimated and an adaptive Asymptotically Uniformly Most Powerful
+(AUMP) test is designed (theoretically and practically), to check whether
+an 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.
+machine learning step, which is often
+implemented as support vector machine,
+can be favorably executed thanks to an ensemble classifier.
\begin{table}
\begin{center}
-\begin{tabular}{|c|c|c|c|}
-Shemes & \multicolumn{2}{|c|}{STABYLO} & HUGO\\
+\begin{tabular}{|c|c|c|c|c|}
\hline
-Embedding rate & Adaptive & 10 \% & 10 \%\\
+Schemes & \multicolumn{3}{|c|}{STABYLO} & HUGO\\
\hline
-AUMP & 0.39 & 0.22 & 0.50 \\
+Embedding & \multicolumn{2}{|c|}{Adaptive} & Fixed & Fixed \\
\hline
-Ensemble Classifier & & & \\
+Rate & + STC & + sample & 10\% & 10\%\\
+\hline
+AUMP & 0.39 & 0.33 & 0.22 & 0.50 \\
+\hline
+Ensemble Classifier & 0.47 & 0.44 & 0.35 & 0.48 \\
\hline
\end{tabular}
\end{table}
-\JFC{Raphael, il faut donner des résultats ici}
\ No newline at end of file
+Results show that our approach is more easily detectable than HUGO, which
+is the most secure steganographic tool, as far as we know. However due to its
+huge number of features integration, it is not lightweight, which justifies
+in authors' opinion the consideration of the proposed method.
+
