comparaison avec HUGO et autres

[canny.git] / ourapproach.tex

diff --git a/ourapproach.tex b/ourapproach.tex
index ccd297b623739e90dec957e0757f307fd97587cc..0f2d7b598d7b08088b6035568d6fe3b027285d6b 100644 (file)
--- a/ourapproach.tex
+++ b/ourapproach.tex
@@ -1,8 +1,8 @@
 The flowcharts given in Fig.~\ref{fig:sch} summarize our steganography scheme denoted as to
 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.
+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}) 
 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{figure*}[t]


@@ -10,8 +10,8 @@ and inside the extraction one(Fig.~\ref{fig:sch:ext}).
     \subfloat[Data Embedding.]{
       \begin{minipage}{0.49\textwidth}
         \begin{center}
     \subfloat[Data Embedding.]{
       \begin{minipage}{0.49\textwidth}
         \begin{center}

-%          \includegraphics[width=5cm]{emb.pdf}
-          \includegraphics[width=5cm]{emb.ps}
+          \includegraphics[width=5cm]{emb.pdf}
+          %\includegraphics[width=5cm]{emb.ps}

         \end{center}
       \end{minipage}
       \label{fig:sch:emb}
         \end{center}
       \end{minipage}
       \label{fig:sch:emb}


@@ -19,8 +19,8 @@ and inside the extraction one(Fig.~\ref{fig:sch:ext}).
     \subfloat[Data Extraction.]{
       \begin{minipage}{0.49\textwidth}
         \begin{center}
     \subfloat[Data Extraction.]{
       \begin{minipage}{0.49\textwidth}
         \begin{center}

-%          \includegraphics[width=5cm]{rec.pdf}
-          \includegraphics[width=5cm]{rec.ps}
+          \includegraphics[width=5cm]{rec.pdf}
+          %\includegraphics[width=5cm]{rec.ps}

         \end{center}
       \end{minipage}
       \label{fig:sch:ext}
         \end{center}
       \end{minipage}
       \label{fig:sch:ext}


@@ -34,52 +34,126 @@ and inside the extraction one(Fig.~\ref{fig:sch:ext}).
 
 
 \subsection{Data Embedding} 
 
 
 \subsection{Data Embedding} 

-
+This section describes the main three steps of the STABYLO data embedding
+scheme. 

 
 
 
 \subsubsection{Edge Based Image Steganography}
 
 
 
 \subsubsection{Edge Based Image Steganography}

+
+
+Edge Based Image Steganography schemes 
+already studied~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10} 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}
 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 :-)}
+\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
 
 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
+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
 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
+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.
 
 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.
 

-Presentation des algos de detection de contour
-Caractéristiques
-
-Comparaison théoriques, références

 
 

-Algo de stegano basé juste sur cela : (pas de bbs, pas de stc, même message).
+% 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 the Canny algorithm 
+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.
+
+% 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.
+
+% 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.

 
 

-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}
+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} 
 
 \subsubsection{Minimizing Distortion with Syndrome-Treillis Codes} 

-

 \input{stc}
 
 
 \subsection{Data Extraction}
 \input{stc}
 
 
 \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.