+
+
+% Results are summarized in Table~\ref{table:quality}.
+% In this table, STC(7) stands for embedding data in the LSB whereas
+% in STC(6), data are hidden in the last two significant bits.
+
+
+% Let us give an interpretation of these experiments.
+% First of all, the Adaptive strategy produces images with lower distortion
+% than the images resulting from the 10\% fixed strategy.
+% Numerical results are indeed always greater for the former strategy than
+% for the latter one.
+% These results are not surprising since the Adaptive strategy aims at
+% embedding messages whose length is decided according to a higher threshold
+% into the edge detection.
+
+
+% If we combine Adaptive and STC strategies
+% the STABYLO scheme provides images whose quality is higher than
+% the EAISLSBMR's one but lower than the quality of high complexity
+% schemes. Notice that the quality of the less respectful scheme (EAILSBMR)
+% is lower than 6\% than the one of the most one.
+
+
+% % Let us now compare the STABYLO approach with other edge based steganography
+% % approaches, namely~\cite{DBLP:journals/eswa/ChenCL10,Chang20101286}.
+% % These two schemes focus on increasing the
+% % payload while the PSNR is acceptable, but do not
+% % give quality metrics for fixed embedding rates from a large base of images.
+
+
+
+
+\subsection{Steganalysis}\label{sub:steg}
+
+
+
+The steganalysis 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 steganalyser.
+Its particularization to spatial domain is
+considered as state of the art steganalysers.
+Firstly, a space
+of 686 co-occurrence and Markov features is 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.
+The final classifier is constructed by a majority voting
+between the decisions of these individual classifiers.
+
+
+%The former approach is based on a simplified parametric model of natural images.
+% 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.
+% This approach is dedicated to verify whether LSB has been modified or not.
+% , the authors show that the
+% machine learning step, which is often
+% implemented as a support vector machine,
+% can be favorably executed thanks to an ensemble classifier.
+
+
+\begin{table*}
