tarata

diff --git a/experiments.tex b/experiments.tex
index 45f0b87967d5052182e0ffe4d39255c77edaf16f..cd2ed1a23e9decfd8794c7fb511859a5c321a823 100644 (file)
--- a/experiments.tex
+++ b/experiments.tex
@@ -9,7 +9,7 @@ Practically, a set of edge pixels is computed according to the
 Canny algorithm with high threshold.
 The message length is thus defined to be the half of this set cardinality.
 The rate between  available bits  and bit message length is then more than two.This constraint is indeed induced by the fact that the efficiency 
 Canny algorithm with high threshold.
 The message length is thus defined to be the half of this set cardinality.
 The rate between  available bits  and bit message length is then more than two.This constraint is indeed induced by the fact that the efficiency 

-of the stc algorithm is unsatisfactory under that threshold.
+of the STC algorithm is unsatisfactory under that threshold.

 
 
 In the latter, the embedding rate is defined as a percentage between the 
 
 
 In the latter, the embedding rate is defined as a percentage between the 


@@ -18,7 +18,7 @@ This is the classical approach adopted in steganography.
 Practically, the Canny algorithm generates a 
 a set of edge pixels with threshold that is decreasing until its cardinality
 is sufficient. If the set cardinality is more than twice larger than the 
 Practically, the Canny algorithm generates a 
 a set of edge pixels with threshold that is decreasing until its cardinality
 is sufficient. If the set cardinality is more than twice larger than the 

-bit message length an stc is again applied.
+bit message length an STC step is again applied.

 Otherwise, pixels are randomly chosen from the set of pixels to build the 
 subset with a given size. The BBS PRNG is again applied there.
  
 Otherwise, pixels are randomly chosen from the set of pixels to build the 
 subset with a given size. The BBS PRNG is again applied there.
  


@@ -27,7 +27,7 @@ subset with a given size. The BBS PRNG is again applied there.
 
 \subsection{Image Quality}
 The visual quality of the STABYLO scheme is evaluated in this section.
 
 \subsection{Image Quality}
 The visual quality of the STABYLO scheme is evaluated in this section.

-Four metrics are computed in these experiments : 
+Four metrics are computed in these experiments: 

 the Peak Signal to Noise Ratio (PSNR), 
 the PSNR-HVS-M family~\cite{PSECAL07,psnrhvsm11} , 
 the BIQI~\cite{MB10,biqi11} and 
 the Peak Signal to Noise Ratio (PSNR), 
 the PSNR-HVS-M family~\cite{PSECAL07,psnrhvsm11} , 
 the BIQI~\cite{MB10,biqi11} and 


@@ -61,9 +61,9 @@ schemes with respect to the image quality.
 Fist off all, wPSNR and PSNR of the Edge Adaptive
 scheme detailed in~\cite{Luo:2010:EAI:1824719.1824720} are lower than ours.
 Next both the approaches~\cite{DBLP:journals/eswa/ChenCL10,Chang20101286}
 Fist off all, wPSNR and PSNR of the Edge Adaptive
 scheme detailed in~\cite{Luo:2010:EAI:1824719.1824720} are lower than ours.
 Next both the approaches~\cite{DBLP:journals/eswa/ChenCL10,Chang20101286}

-focus on increasing the payload while the PSNR is acceptable, bu do not 
+focus on increasing the payload while the PSNR is acceptable, but do not 

 give quality metrics for fixed embedding rate from a large base of images. 
 give quality metrics for fixed embedding rate from a large base of images. 

-Our approach outperforms the former thanks to the introduction of the stc 
+Our approach outperforms the former thanks to the introduction of the STC 

 algorithm.
 
 
 algorithm.
 
 


@@ -97,7 +97,7 @@ Embedding rate &  Adaptive & 10 \% &  10 \%\\
 \hline
 AUMP & 0.39  & 0.22     &  0.50     \\
 \hline
 \hline
 AUMP & 0.39  & 0.22     &  0.50     \\
 \hline

-Ensemble Classifier &   &      &      \\
+Ensemble Classifier & 0.47   & 0.35     & 0.48     \\

 
 \hline
 \end{tabular}
 
 \hline
 \end{tabular}

diff --git a/intro.tex b/intro.tex
index 745751fd2ce63effdb2e630fadc6be8887d84172..f69d92647c6aff809fedd5231dd3fb5fc2225ad7 100644 (file)
--- a/intro.tex
+++ b/intro.tex
@@ -62,7 +62,8 @@ Our proposal is thus to embed message bits into edge shapes while preserving oth
 
 Edge based steganographic schemes have been already studied~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10}.
 In the former, the authors show how to select sharper edge regions with respect 
 
 Edge based steganographic schemes have been already studied~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10}.
 In the former, the authors show how to select sharper edge regions with respect 

-to embedding rate: the larger the number of bits to be embedded, the coarse the edge regions are.
+to embedding rate: the larger the number of bits to be embedded, the coarser
+the edge regions are.

 Then the data hiding algorithm is achieved by applying LSBMR on pixels of this region. 
 The authors show that this method is more efficient than all the LSB, LSBM, LSBMR approaches 
 thanks to extensive experiments.
 Then the data hiding algorithm is achieved by applying LSBMR on pixels of this region. 
 The authors show that this method is more efficient than all the LSB, LSBM, LSBMR approaches 
 thanks to extensive experiments.

diff --git a/ourapproach.tex b/ourapproach.tex
index f832695dc830ca094bc87b1cbfd1aa9c50ddfece..0f2d7b598d7b08088b6035568d6fe3b027285d6b 100644 (file)
--- a/ourapproach.tex
+++ b/ourapproach.tex
@@ -1,5 +1,5 @@
 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}) 
 and inside the extraction one (Fig.~\ref{fig:sch:ext}).
 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}).


@@ -43,7 +43,7 @@ scheme.
 
 
 Edge Based Image Steganography schemes 
 
 
 Edge Based Image Steganography schemes 

-already studied~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10,DBLP:conf/ih/PevnyFB10} differ 
+already studied~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10} differ 

 how they select edge pixels, and  
 how they modify these ones.
 
 how they select edge pixels, and  
 how they modify these ones.
 


@@ -51,61 +51,62 @@ 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}
 \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.
 
 

-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.   
+% 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
 
 Next, following~\cite{Luo:2010:EAI:1824719.1824720}, our scheme automatically

-modifies canny parameters to get a sufficiently large set of edge bits: this 
+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.
 
 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.
+% 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{Security Considerations}
 
 
 \subsubsection{Security Considerations}