X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/24da70a2907152b45f175222de4962951669ac12..e1408e22528148d27bf1cc1edc56764345c64cba:/experiments.tex?ds=sidebyside

diff --git a/experiments.tex b/experiments.tex
index b952fae..cd2ed1a 100644
--- 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.
-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 familly~\cite{PSECAL07,psnrhvsm11} , 
+the PSNR-HVS-M family~\cite{PSECAL07,psnrhvsm11} , 
 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.
@@ -14,10 +40,9 @@ The other last ones have been designed to tackle this problem.
 \begin{center}
 \begin{tabular}{|c|c|c|}
 \hline
-Embedding rate &  Adaptive 
-10 \% &  \\
+Embedding rate &  Adaptive & 10 \%  \\
 \hline
-PSNR &      &      \\
+PSNR &  66.55    & 61.86     \\
 \hline
 PSNR-HVS-M & 78.6  & 72.9 \\
 \hline
@@ -27,14 +52,19 @@ wPSNR & 86.43& 77.47 \\
 \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}
 
 
-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}
@@ -60,13 +90,14 @@ can be a favourably executed thanks to an Ensemble Classiï¬ers.
 \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
-Ensemble Classifier &   &      &      \\
+Ensemble Classifier & 0.47   & 0.35     & 0.48     \\
 
 \hline
 \end{tabular}
@@ -75,3 +106,7 @@ Ensemble Classifier &   &      &      \\
 \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.   
+
