For whole experiments, the whole set of 10,000 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 focused on 
the methodology than benchmarking.    

We use the matrices $\hat{H}$ 
generated by the integers given
in table~\ref{table:matrices:H}
as introduced in~\cite{FillerJF11}, since these ones have experimentally 
be proven to have the best modification efficiency.
For instance if the rate between the size of message and the size of the host is 
1/4, each number in $\{81, 95, 107, 121\}$ is translated into a binary number 
and each one consitutes thus an column of $\hat{H}$. 

\begin{table}
$$
\begin{array}{|l|l|}
\hline
\textrm{Rate} & \textrm{Matrix generators} \\
\hline
{1}/{2} & \{71,109\}\\
\hline
{1}/{3} & \{95, 101, 121\}\\
\hline
{1}/{4} & \{81, 95, 107, 121\}\\
\hline
{1}/{5} & \{75, 95, 97, 105, 117\}\\
\hline
{1}/{6} & \{73, 83, 95, 103, 109, 123\}\\
\hline
{1}/{7} & \{69, 77, 93, 107, 111, 115, 121\}\\
\hline
{1}/{8} & \{69, 79, 81, 89, 93, 99, 107, 119\}\\
\hline
{1}/{9} & \{69, 79, 81, 89, 93, 99, 107, 119, 125\}\\
\hline
\end{array}
$$
\caption{Matrix Generator for $\hat{H}$ in STC}\label{table:matrices:H}
\end{table}


Our approach is always compared to Hugo~\cite{DBLP:conf/ih/PevnyFB10}
and to EAISLSBMR~\cite{Luo:2010:EAI:1824719.1824720}.
The former is the least detectable information hiding tool in spatial domain 
and the latter is the work that is the closest to ours, as far as we know. 



First of all,  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\%. 
The two other tools will then be compared with this payload. 
Sections~\ref{sub:quality} and~\ref{sub:steg} respectively present 
the quality analysis and the security of our scheme. 



 

\subsection{Image quality}\label{sub:quality}
The visual quality of the STABYLO scheme is evaluated in this section.
For the sake of completeness, three 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.

If we apply them on the running example, 
the PSNR, PSNR-HVS-M, and wPSNR values are respectively equal to 
68.39, 79.85, and 89.71 for the stego Lena when $b$ is equal to 7.
If $b$ is 6, these values are respectively equal to 
65.43, 77.2, and 89.35.




\begin{table*}
\begin{center}
\begin{tabular}{|c|c|c||c|c|c|c|c|c|}
\hline
Schemes & \multicolumn{4}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR} \\
\hline
Embedding &   Fixed & \multicolumn{3}{|c|}{Adaptive (about 6.35\%)} &  \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
\hline
Rate &   10\% &  + sample &  +STC(7) & +STC(6) &  10\%&6.35\%& 10\%&6.35\%\\ 
\hline
PSNR & 61.86 & 63.48 &  66.55 (\textbf{-0.8\%}) &  63.7  & 64.65 & {67.08} & 60.8 & 62.9\\ 
\hline
PSNR-HVS-M & 72.9 & 75.39 & 78.6 (\textbf{-0.8\%}) & 75.5  & 76.67 & {79.23} & 71.8  & 74.3\\ 
%\hline
%BIQI & 28.3 & 28.28 & 28.4 & 28.28 & 28.28 & 28.2 & 28.2\\ 
\hline
wPSNR & 77.47 & 80.59 & 86.43(\textbf{-1.6\%})& 86.28  & 83.03 & {87.8} & 76.7 & 80.6\\ 
\hline
\end{tabular}

\begin{footnotesize}
\vspace{2em}
Variances given in bold font express the quality differences between 
HUGO and STABYLO with  STC+adaptive parameters.
\end{footnotesize}

\end{center}
\caption{Quality measures of steganography approaches\label{table:quality}}
\end{table*}



Results are summarized in Table~\ref{table:quality}.
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 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 is always better than EAISLSBMR since this one may modify 
the two least significant bits.

If we combine \emph{adaptive} and \emph{STC} strategies 
(which leads to an average embedding rate equal to 6.35\%)
our approach  provides metrics equivalent to those provided by HUGO.
In this column STC(7) stands for embedding data in the LSB whereas
in STC(6), data are hidden in the two last significant bits. 



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}\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 steganalysers.
Both aim at detecting hidden bits in grayscale natural images and are 
considered as state of the art steganalysers in the 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 a support vector machine,
can be favorably executed thanks to an ensemble classifier.


\begin{table*}
\begin{center}
%\begin{small}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
\hline
Schemes & \multicolumn{4}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR}\\
\hline
Embedding & Fixed &   \multicolumn{3}{|c|}{Adaptive (about 6.35\%)}  & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
\hline
Rate & 10\% &  + sample &   +STC(7) & +STC(6)   & 10\%& 6.35\%& 10\%& 6.35\%\\ 
\hline
AUMP & 0.22 & 0.33 & 0.39  &   0.45    &  0.50 &  0.50 & 0.49 & 0.50 \\
\hline
Ensemble Classifier & 0.35 & 0.44 & 0.47 & 0.47     & 0.48 &  0.49  &  0.43  & 0.46 \\

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


Results are summarized in Table~\ref{table:steganalyse}.
First of all, STC outperforms the sample strategy for the two steganalysers, as 
already noticed in the quality analysis presented in the previous section. 
Next, our approach is more easily detectable than HUGO, which
is the most secure steganographic tool, as far as we know. 
However by combining \emph{adaptive} and \emph{STC} strategies
our approach obtains similar results to HUGO ones.

However due to its 
huge number of integration features, it is not lightweight, which justifies 
in the authors' opinion the consideration of the proposed method.   

