tarata

[canny.git] / experiments.tex

diff --git a/experiments.tex b/experiments.tex
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--- a/experiments.tex
+++ b/experiments.tex
@@ -1,11 +1,37 @@
+\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 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.
+ 
+
+ 

 
 \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 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.


@@ -14,10 +40,9 @@ 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
 \hline

-PSNR &      &      \\
+PSNR &  66.55    & 61.86     \\

 \hline
 PSNR-HVS-M & 78.6  & 72.9 \\
 \hline
 \hline
 PSNR-HVS-M & 78.6  & 72.9 \\
 \hline


@@ -27,14 +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, but 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}


@@ -60,13 +90,14 @@ 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
 AUMP & 0.39  & 0.22     &  0.50     \\
 \hline
 \hline
 Embedding rate &  Adaptive & 10 \% &  10 \%\\
 \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}


@@ -75,3 +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.   
+