modifs avant vacances

diff --git a/biblio.bib b/biblio.bib
index 22bcda23a77bdf4c8fbca773ae1d1af89b1c2f00..3c06dc7406c36a8f1e0b47ba2a17b5a929dce0ed 100644 (file)
--- a/biblio.bib
+++ b/biblio.bib
@@ -25,6 +25,28 @@
   isbn      = {3-540-42733-3},
   bibsource = {DBLP, http://dblp.uni-trier.de}
 }
   isbn      = {3-540-42733-3},
   bibsource = {DBLP, http://dblp.uni-trier.de}
 }

+
+@article{Chang20101286,
+title = "Hybrid wet paper coding mechanism for steganography employing n-indicator and fuzzy edge detector",
+journal = "Digital Signal Processing",
+volume = "20",
+number = "4",
+pages = "1286 - 1307",
+year = "2010",
+note = "",
+issn = "1051-2004",
+doi = "10.1016/j.dsp.2009.11.005",
+url = "http://www.sciencedirect.com/science/article/pii/S1051200409002413",
+author = "Chin-Chen Chang and Jung-San Lee and T. Hoang Ngan Le",
+keywords = "Security",
+keywords = "Wet paper coding",
+keywords = "Steganography",
+keywords = "Indicator",
+keywords = "Fuzzy edge detector"
+}
+
+
+

 @article{Hu:2007:HPE:1282866.1282944,
  author = {Hu, Liming and Cheng, H. D. and Zhang, Ming},
  title = {A high performance edge detector based on fuzzy inference rules},
 @article{Hu:2007:HPE:1282866.1282944,
  author = {Hu, Liming and Cheng, H. D. and Zhang, Ming},
  title = {A high performance edge detector based on fuzzy inference rules},


@@ -203,7 +225,7 @@ author    = {Jessica J. Fridrich and
   interhash = {bc34a5f04661fee24ee62c39e76361be},
   intrahash = {28889a4ab329da28559f0910469f054b},
   journal = {Expert Syst. Appl.},
   interhash = {bc34a5f04661fee24ee62c39e76361be},
   intrahash = {28889a4ab329da28559f0910469f054b},
   journal = {Expert Syst. Appl.},

-  keywords = {dblp},
+  keywords = {dblp}, 

   number = 4,
   pages = {3292-3301},
   timestamp = {2010-07-21T15:44:10.000+0200},
   number = 4,
   pages = {3292-3301},
   timestamp = {2010-07-21T15:44:10.000+0200},

diff --git a/experiments.tex b/experiments.tex
index 0011cd4ef7e24952f02aa34a4cda99281614e711..45f0b87967d5052182e0ffe4d39255c77edaf16f 100644 (file)
--- a/experiments.tex
+++ b/experiments.tex
@@ -1,14 +1,37 @@
 \subsection{Adaptive Embedding Rate} 
 
 \subsection{Adaptive Embedding Rate} 
 

-
+Two strategies have been developed in our scheme with respect to the rate of 
+embedding which is either \emph{ adaptive} or \emph{fixed}.
+
+In the former the embedding rate depends on the number of edge pixels.
+The higher it is, the larger is the message length that can be considered.
+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 
+of the stc algorithm is unsatisfactory under that threshold.
+
+
+In the latter, the embedding rate is defined as a percentage between the 
+number of the modified pixels and the length of the bit message.
+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 
+bit message length an stc 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.
+ 
+
+ 

 
 \subsection{Image Quality}
 The visual quality of the STABYLO scheme is evaluated in this section.
 Four metrics are computed in these experiments : 
 the Peak Signal to Noise Ratio (PSNR), 
 
 \subsection{Image Quality}
 The visual quality of the STABYLO scheme is evaluated in this section.
 Four metrics are computed in these experiments : 
 the Peak Signal to Noise Ratio (PSNR), 

-the PSNR-HVS-M familly~\cite{PSECAL07,psnrhvsm11} , 
+the PSNR-HVS-M family~\cite{PSECAL07,psnrhvsm11} , 

 the BIQI~\cite{MB10,biqi11} and 
 the BIQI~\cite{MB10,biqi11} and 

-the weigthed PSNR (wPSNR)~\cite{DBLP:conf/ih/PereiraVMMP01}.
+the weighted PSNR (wPSNR)~\cite{DBLP:conf/ih/PereiraVMMP01}.

 The first one is widely used but does not take into
 account Human Visual System (HVS).
 The other last ones have been designed to tackle this problem.
 The first one is widely used but does not take into
 account Human Visual System (HVS).
 The other last ones have been designed to tackle this problem.


@@ -17,8 +40,7 @@ The other last ones have been designed to tackle this problem.
 \begin{center}
 \begin{tabular}{|c|c|c|}
 \hline
 \begin{center}
 \begin{tabular}{|c|c|c|}
 \hline

-Embedding rate &  Adaptive 
-10 \% &  \\
+Embedding rate &  Adaptive & 10 \%  \\

 \hline
 PSNR &  66.55    & 61.86     \\
 \hline
 \hline
 PSNR &  66.55    & 61.86     \\
 \hline


@@ -30,14 +52,19 @@ wPSNR & 86.43& 77.47 \\
 \hline
 \end{tabular}
 \end{center}
 \hline
 \end{tabular}
 \end{center}

-\caption{Quality measeures of our steganography approach\label{table:quality}} 
+\caption{Quality measures of our steganography approach\label{table:quality}} 

 \end{table}
 
 
 \end{table}
 
 

-Compare to the Edge Adpative scheme detailed in~\cite{Luo:2010:EAI:1824719.1824720}, our both wPSNR and PSNR values are always higher than their ones.
-
-\JFC{comparer aux autres approaches}
-
+Let us compare the STABYLO approach with other edge based steganography
+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}
+focus on increasing the payload while the PSNR is acceptable, bu do not 
+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 
+algorithm.

 
 
 \subsection{Steganalysis}
 
 
 \subsection{Steganalysis}


@@ -63,7 +90,8 @@ can be a favourably executed thanks to an Ensemble Classifiers.
 \begin{table}
 \begin{center}
 \begin{tabular}{|c|c|c|c|}
 \begin{table}
 \begin{center}
 \begin{tabular}{|c|c|c|c|}

-Shemes & \multicolumn{2}{|c|}{STABYLO} & HUGO\\
+\hline
+Schemes & \multicolumn{2}{|c|}{STABYLO} & HUGO\\

 \hline
 Embedding rate &  Adaptive & 10 \% &  10 \%\\
 \hline
 \hline
 Embedding rate &  Adaptive & 10 \% &  10 \%\\
 \hline


@@ -78,3 +106,7 @@ Ensemble Classifier &   &      &      \\
 \end{table}
 
 
 \end{table}
 
 

+Results show that our approach is more easily detectable than HUGO which is
+is the more secure steganography tool, as far we know. However due to its 
+huge number of features integration, it is not lightweight.   
+