apres relecture Christophe

[canny.git] / ourapproach.tex

diff --git a/ourapproach.tex b/ourapproach.tex
index ccd297b623739e90dec957e0757f307fd97587cc..3319b6a26a317c008a28c1df87d7e666fece6d1a 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
-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}).
+The flowcharts given in Fig.~\ref{fig:sch} summarize our steganography scheme denoted by
+STABYLO, which stands for STeganography with cAnny, Bbs, binarY embedding at LOw cost.
+What follows successively details all the inner steps and flows inside 
+both the embedding stage (Fig.~\ref{fig:sch:emb}) 
+and 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 +34,144 @@ 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}
+
+
+The edge-based image steganography schemes 
+already presented \cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10} differ 
+in how carefully they select edge pixels, and  
+how they modify them.
+
+%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 :-)}
+
+Many techniques have been proposed in the literature to  detect 
+edges in  images (whose noise has been initially reduced). 
+They can be separated in two categories: first and second order detection
+methods on the one hand, and fuzzy detectors in the second hand~\cite{KF11}.
+In first order methods like Sobel,
+a first-order derivative (gradient magnitude, etc.) is computed 
+to search for local maxima, whereas in second order ones, zero crossings in a second-order derivative, like the Laplacian computed from the image,
+are searched in order to find edges.
+For fuzzy edge methods, they are obviously based on fuzzy logic to highlight
+edges.
+Canny filters, on their parts, are an old family of algorithms still remaining a state-of-the-art edge detector. They can be well approximated by first-order derivatives of Gaussians.
+%%
+%
+%Of course, all the algorithms have  advantages and drawbacks that depend on the
+%motivations behind that edges detection.   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
+As Canny algorithm is well known and studied, fast, and implementable
+on many  kinds of architectures like FPGAs, smartphones,  desktop machines, and
+GPUs, we have chosen this edge detector for illustrative purpose.
+Of course, other detectors like the fuzzy edge methods
+merit much further attention, which is why we intend 
+to investigate systematically all of these detectors in our next work.
+%we do not pretend that this is the best solution.
+
+In order to be able to compute the same set of edge pixels, we suggest to consider all the bits of the image (cover or stego) without the LSB. Thus, with an 8 bits image, only the 7 first bits are considered. In our flowcharts, this is represented by ``LSB(7 bits Edge Detection)''.
+% 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 until its size is at least twice as large 
+as the size of the 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.

 
 
 
 

-
-
-\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. } \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.
-
-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).
-
-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}
+that is based on the Blum Blum Shub~\cite{DBLP:conf/crypto/ShubBB82} pseudorandom 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 of $L$ 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 $1/2$.
+Equivalent formulations of such a property can
+be found. They all lead to the fact that,
+even if the encrypted message is extracted, 
+it is impossible to retrieve the original one in 
+polynomial time.   
+
+
+%%RAPH: paragraphe en double :-)
+
+%% \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 (on the 7 first bits) to 
+get the set of LSBs,
+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.