-The flowcharts given in Fig.~\ref{fig:sch} summarize our steganography scheme denoted as
+The flowcharts given in Fig.~\ref{fig:sch} summarize our steganography scheme denoted as to
STABYLO for STeganography with cAnny, Bbs, binarY embedding at LOw cost.
What follows successively details all the inner steps and flow inside
the embedding stage (Fig.\ref{fig:sch:emb})
-and inside the extraction one(Fig.~\ref{fig:sch:ext}).
+and inside the extraction one (Fig.~\ref{fig:sch:ext}).
\begin{figure*}[t]
\begin{minipage}{0.49\textwidth}
\begin{center}
\includegraphics[width=5cm]{emb.pdf}
+ %\includegraphics[width=5cm]{emb.ps}
\end{center}
\end{minipage}
\label{fig:sch:emb}
\begin{minipage}{0.49\textwidth}
\begin{center}
\includegraphics[width=5cm]{rec.pdf}
+ %\includegraphics[width=5cm]{rec.ps}
\end{center}
\end{minipage}
\label{fig:sch:ext}
\end{figure*}
-\subsection{Steganalysis}
-LSB :
-"Adaptive steganalysis of Least Significant Bit replacement in grayscale natural images"
-Structural LSB Detectors:
-\verb+http://dde.binghamton.edu/download/structural_lsb_detectors/+
-
-Vainqueur du BOSS challenge
-
-
-ensemble:
-
-
-G. Gül and F. Kurugollu. A new methodology in steganalysis : Breaking highly
-undetactable steganograpy (HUGO). In Information Hiding, 13th International
-Workshop, volume 6958 of LNCS, pages 71–84, Prague, Czech Republic, May 18–
-20, 2011.
+\subsection{Data Embedding}
+This section describes the main three steps of the STABYLO data embedding
+scheme.
-\subsection{Data Embedding}
+\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.
-\subsubsection{Edge Based Image Steganography}
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. }
-
-
-Presentation des algos de detection de contour
-Caractéristiques
-
-Comparaison théoriques, références
+ 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},
+even if the fuzzy logic based edge detection methods~\cite{Tyan1993}
+have promising results, its complexity is in $C_3 \times O(M \times N)$
+whereas the complexity on the Canny method~\cite{Canny:1986:CAE:11274.11275}
+is in $C_1 \times O(M \times N)$ where $C_1 < C_3$.
+\JFC{Verifier ceci...}
+In experiments detailled in this article, the canny method has been retained
+but the whole approach can be updated to consider
+the fuzzy logic edge detector.
+
+Next, following~\cite{Luo:2010:EAI:1824719.1824720}, our scheme automatically
+modifies canny parameters to get a sufficiently large set of edge bits: this
+one is practically enlarged untill its size is at least twice as many larger
+than the size of embedded message.
-Algo de stegano basé juste sur cela : (pas de bbs, pas de stc, même message).
-Quels paramètres sont optimaux ? Combinaison ?
+\subsubsection{Security Considerations}
+Among methods of message encryption/decryption
+(see~\cite{DBLP:journals/ejisec/FontaineG07} for a survey)
+we implement the Blum-Goldwasser cryptosystem~\cite{Blum:1985:EPP:19478.19501}
+which is based on the Blum Blum Shub~\cite{DBLP:conf/crypto/ShubBB82} Pseudo Random Number Generator (PRNG)
+for security reasons.
+It has been indeed proven~\cite{DBLP:conf/crypto/ShubBB82} that this PRNG
+has the cryptographically security property, \textit{i.e.},
+for any sequence $L$ of output bits $x_i$, $x_{i+1}$, \ldots, $x_{i+L-1}$,
+there is no algorithm, whose time complexity is polynomial in $L$, and
+which allows to find $x_{i-1}$ and $x_{i+L}$ with a probability greater
+than $1/2$.
+Thus, even if the encrypted message would be extracted,
+it would thus be not possible to retrieve the original one in a
+polynomial time.
-\subsubsection{Security Considerations}
-Security aspect:
-BBS-based cryptographic version of the message
\subsubsection{Minimizing Distortion with Syndrome-Treillis Codes}
-
\input{stc}
-\subsection{Data Extraction}
\ No newline at end of file
+\subsection{Data Extraction}
+Message extraction summarized in Fig.~\ref{fig:sch:ext} follows data embedding
+since there exists a reverse function for all its steps.
+First of all, the same edge detection is applied to get set,
+which is sufficiently large with respect to the message size given as a key.
+Then the STC reverse algorithm is applied to retrieve the encrypted message.
+Finally, the Blum-Goldwasser decryption function is executed and the original
+message is extracted.
