For whole experiments, the whole set of 10000 images 
of the BOSS contest~\cite{Boss10} database is taken. 
In this set, each cover is a $512\times 512$
grayscale digital image in a RAW format. 
We restrict experiments to 
this set of cover images since this paper is more focussed on 
the methodology than benchmarking.    
Our approach is always compared to Hugo~\cite{DBLP:conf/ih/PevnyFB10}
and to EAISLSBMR~\cite{Luo:2010:EAI:1824719.1824720}.





\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 the message length that can be inserted is.
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 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 to 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.
In our experiments and with the adaptive scheme, 
the average size of the message that can be embedded is 16,445 bits.
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 set of edge pixels related to a 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.
For the sake of completeness, four metrics are computed in these experiments: 
the Peak Signal to Noise Ratio (PSNR), 
the PSNR-HVS-M family~\cite{psnrhvsm11}, 
%the BIQI~\cite{MB10}, 
and 
the weighted PSNR (wPSNR)~\cite{DBLP:conf/ih/PereiraVMMP01}.
The first one is widely used but does not take into
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||c|c|c|c|c|}
\hline
Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR} \\
\hline
Embedding &   \multicolumn{2}{|c||}{Adaptive} & Fixed & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
\hline
Rate &   + STC &  + sample & 10\% & 10\%&6.35\%& 10\%&6.35\%\\ 
\hline
PSNR &  66.55 (\textbf{-0.8\%}) & 63.48  & 61.86  & 64.65 & {67.08} & 60.8 & 62.9\\ 
\hline
PSNR-HVS-M & 78.6 (\textbf{-0.8\%})  & 75.39 & 72.9 & 76.67 & {79.23} & 61.3  & 63.4\\ 
%\hline
%BIQI & 28.3 & 28.28 & 28.4 & 28.28 & 28.28 & 28.2 & 28.2\\ 
\hline
wPSNR & 86.43(\textbf{-1.6\%}) & 80.59 & 77.47& 83.03 & {87.8} & & 80.6\\ 
\hline
\end{tabular}
\end{center}
\caption{Quality Measures of Steganography Approaches\label{table:quality}}
\label{table:quality}
\end{table*}



Results are summarized into the Table~\ref{table:quality}.
Let us give an interpretation of these first 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.
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 appraoch always outperforms EAISLSBMR since this one may modify 
the two least significant bits whereas STABYLO only alter LSB.

If we combine \emph{adaptive} and \emph{STC} strategies 
(which leads to an average embedding rate equal to 6.35\%)
our approach  provides equivalent metrics than HUGO.
The quality variance between HUGO and STABYLO for these parameters
is given in bold font. It is always close to 1\% which confirms 
the objective presented in the motivations:
providing an efficient steganography approach with a lightweight manner.


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}



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 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.
In the latter, the authors show that the 
machine learning step, which is often
implemented as support vector machine,
can be favorably executed thanks to an ensemble classifier.

%citer le second tableau, comparer avec EAISLSBMR

\begin{table*}
\begin{center}
%\begin{small}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
\hline
Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR}\\
\hline
Embedding &   \multicolumn{2}{|c|}{Adaptive} & Fixed & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
\hline
Rate &   + STC &  + sample & 10\% & 10\%& 6.35\%& 10\%& 6.35\%\\ 
\hline
AUMP & 0.39  & 0.33  & 0.22     &  0.50 &  0.50 & 0.49 & 0.50 \\
\hline
Ensemble Classifier & \textbf{0.47} & 0.44 & 0.35     & 0.48 &  0.49  &  0.43  & 0.46 \\

\hline
\end{tabular}
%\end{small}
\end{center}
\caption{Steganalysing STABYLO\label{table:steganalyse}} 
\end{table*}


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 the authors' opinion the consideration of the proposed method.   

