\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}



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.   

