pixels. In other words, minor changes in regular area are more dramatic than larger modifications in edge ones.
Our proposal is thus to embed message bits into edge shapes while preserving other smooth regions.
-Edge based steganographic schemes have bee already studied~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10}.
+Edge based steganographic schemes have been already studied~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10}.
In the former, the authors show how to select sharper edge regions with respect
to embedding rate: the larger the number of bits to be embedded, the coarse the edge regions are.
Then the data hiding algorithm is achieved by applying LSBMR on pixels of this region.
produce stego content
with only considering the payload, not the type of image signal: the higher the payload is,
the better the approach is said to be.
-Contrarely, we argue that some images should'nt be taken as a cover because of the nature of their signal.
+Contrarely, we argue that some images should not be taken as a cover because of the nature of their signal.
Consider for instance a uniformly black image: a very tiny modification of its pixels can be easily detectable.
The approach we propose is thus to provide a self adaptive algorithm with a high payload, which depends on the
cover signal.
\subsubsection{Edge Based Image Steganography}
+
Edge Based Image Steganography schemes
already studied~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10,DBLP:conf/ih/PevnyFB10} differ
how they select edge pixels, and
how they modify these ones.
+Image Quality: Edge Image Steganography
+\JFC{Raphael, les fuzzy edge detection sont souvent utilisés.
+ il faudrait comparer les approches en terme de nombre de bits retournés,
+ en terme de complexité. } \cite{KF11}
+\RC{Ben, à voir car on peut choisir le nombre de pixel avec canny. Supposons que les fuzzy edge soient retourne un peu plus de points, on sera probablement plus détectable... Finalement on devrait surement vendre notre truc en : on a choisi cet algo car il est performant en vitesse/qualité. Mais on peut aussi en utilisé d'autres :-)}
+
+There are many techniques to detect edges in images. Main methods are filter
+edge detection methods such as Sobel or Canny filter, low order methods such as
+first order and second order methods, these methods are based on gradient or
+Laplace operators and fuzzy edge methods which are based on fuzzy logic to
+highlight edges.
+
+Of course, all the algorithms have advantages and drawbacks which depend on the
+motivation to highlight edges. Unfortunately unless testing most of the
+algorithms, which would require many times, it is quite difficult to have an
+accurate idea on what would produce such algorithm compared to another. That is
+why we have chosen canny algorithm which is well known, fast and implementable
+on many kinds of architecture, such as FPGA, smartphone, desktop machines and
+GPU. And of course, we do not pretend that this is the best solution.
+
+
First of all, let us discuss about compexity of edge detetction methods.
Let then $M$ and $N$ be the dimension of the original image.
According to~\cite{Hu:2007:HPE:1282866.1282944},
