maj

[canny.git] / ourapproach.tex

diff --git a/ourapproach.tex b/ourapproach.tex
index b49c44971ebb7308a12d1945a25dad564a6e2f7b..74c1b559328204e23d72b72e1ca7032b4d0ae23f 100644 (file)
--- a/ourapproach.tex
+++ b/ourapproach.tex
@@ -1,9 +1,10 @@
-The flowcharts given in Fig.~\ref{fig:sch} summarize our steganography scheme denoted by
+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}).
 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}
 
 \begin{figure*}[t]
   \begin{center}


@@ -11,7 +12,7 @@ and the extraction one (Fig.~\ref{fig:sch:ext}).
       \begin{minipage}{0.49\textwidth}
         \begin{center}
           %\includegraphics[width=5cm]{emb.pdf}
       \begin{minipage}{0.49\textwidth}
         \begin{center}
           %\includegraphics[width=5cm]{emb.pdf}

-          \includegraphics[width=5cm]{emb.ps}
+          \includegraphics[scale=0.5]{emb.ps}

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


@@ -20,7 +21,7 @@ and the extraction one (Fig.~\ref{fig:sch:ext}).
       \begin{minipage}{0.49\textwidth}
         \begin{center}
           %\includegraphics[width=5cm]{rec.pdf}
       \begin{minipage}{0.49\textwidth}
         \begin{center}
           %\includegraphics[width=5cm]{rec.pdf}

-          \includegraphics[width=5cm]{rec.ps}
+          \includegraphics[scale=0.5]{rec.ps}

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


@@ -33,16 +34,38 @@ and the extraction one (Fig.~\ref{fig:sch:ext}).
 
 
 
 
 
 

-\subsection{Data Embedding} 
-This section describes the main three steps of the STABYLO data embedding
-scheme. 

 
 
 
 
 
 

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

 
 
 
 

-The edge-based image steganography schemes 
+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.
 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.


@@ -57,30 +80,32 @@ 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}.
 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,
+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.
 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.
+As for as fuzzy edge methods are concerned, 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.
 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 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.
 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.

-Of course, other detectors like the fuzzy edge methods
-deserve 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)''.
+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},
 % 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},


@@ -92,11 +117,23 @@ In order to be able to compute the same set of edge pixels, we suggest to consid
 % 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.   
 % 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.
+
+
+
+
+
+
+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}
+
+

 
 % Edge Based Image Steganography schemes 
 % already studied~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10,DBLP:conf/ih/PevnyFB10} differ 
 
 % Edge Based Image Steganography schemes 
 % already studied~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10,DBLP:conf/ih/PevnyFB10} differ 


@@ -121,51 +158,12 @@ as the size of the embedded message.
 % than the size of embedded message.
 
 
 % than the size of embedded message.
 
 

-\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 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.   
-

 
 %%RAPH: paragraphe en double :-)
 
 
 %%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{Minimizing Distortion with Syndrome-Treillis Codes} 
-\input{stc}
-
-

 \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.
 \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.