maj

[canny.git] / ourapproach.tex

diff --git a/ourapproach.tex b/ourapproach.tex
index ccd297b623739e90dec957e0757f307fd97587cc..74c1b559328204e23d72b72e1ca7032b4d0ae23f 100644 (file)
--- a/ourapproach.tex
+++ b/ourapproach.tex
@@ -1,17 +1,18 @@
-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 are successively details of the inner steps and flows inside 
+both the embedding stage (Fig.~\ref{fig:sch:emb}) 
+and the extraction one (Fig.~\ref{fig:sch:ext}).
+Let us first focus on the data embedding. 

 
 \begin{figure*}[t]
   \begin{center}
     \subfloat[Data Embedding.]{
       \begin{minipage}{0.49\textwidth}
         \begin{center}
 
 \begin{figure*}[t]
   \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[scale=0.5]{emb.ps}

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


@@ -19,8 +20,8 @@ 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[scale=0.5]{rec.ps}

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


@@ -33,53 +34,142 @@ and inside the extraction one(Fig.~\ref{fig:sch:ext}).
 
 
 
 
 
 

-\subsection{Data Embedding} 

 
 
 
 
 
 
 
 

-\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 :-)}
+\subsection{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) and the 
+XOR binary function.
+It has been indeed proven~\cite{DBLP:conf/crypto/ShubBB82} that this PRNG 
+has the property of cryptographical security, \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.   
+
+Starting thus with a key $k$ and the message \textit{mess} to hide, 
+this step computes a message $m$, which is the encrypted version  of \textit{mess}.
+
+
+\subsection{Edge-Based Image Steganography}

 
 

-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.
+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.

 
 

-Presentation des algos de detection de contour
-Caractéristiques
+%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 :-)}

 
 

-Comparaison théoriques, références
+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 on the other  hand~\cite{KF11}.
+In first order methods like Sobel, Canny~\cite{Canny:1986:CAE:11274.11275}, \ldots, 
+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.
+As for as fuzzy edge methods are concerned, they are obviously based on fuzzy logic to highlight edges.

 
 

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

 
 

-Quels paramètres sont optimaux ? Combinaison ? 
+This edge detection is applied on a filtered version of the image given 
+as input.
+More precisely, only $b$ most 
+significant bits are concerned by this step, where 
+the parameter $b$ is practically set with $6$ or $7$. 
+If set with the same value $b$, the edge detection returns thus the same 
+set of pixels for both the cover and the stego image.   
+In our flowcharts, this is represented by ``edgeDetection(b bits)''.
+Then only the 2 LSBs of pixels in the set of edges are returned if $b$ is 6, 
+and the LSB of pixels if $b$ is 7.
+Let $x$ be the sequence of these bits.
+ 
+\JFC{il faudrait comparer les complexites des algo fuzy and canny}

 
 
 
 

+% 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.   

 
 
 
 
 
 

-\subsubsection{Security Considerations}

 
 

-Security aspect:
-BBS-based cryptographic version of the message 

 
 

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

 
 

+As argue in the introduction section, we do not adapt the parameters of the
+the edge detection as in~\cite{Luo:2010:EAI:1824719.1824720} but we modify 
+the size of the embedding message. Practically, the lenght of $x$ 
+has to be at least twice as large 
+as the size of the embedded encrypted message. 
+Otherwise, a new image is used to hide the remaning part of the message.  
+
+\subsection{Minimizing Distortion with Syndrome-Treillis Codes} 

 \input{stc}
 
 
 \input{stc}
 
 

+
+% 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.
+
+
+
+%%RAPH: paragraphe en double :-)
+
+
+
+

 \subsection{Data Extraction}
 \subsection{Data Extraction}

+The 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.