X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/4516d82b16c946a9e8a9cadd0011616e8a66cc31..69853fcf95c4cb941d699fd5f1ca32540189b8a9:/experiments.tex?ds=inline

diff --git a/experiments.tex b/experiments.tex
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--- a/experiments.tex
+++ b/experiments.tex
@@ -1,39 +1,110 @@
+For the whole experiment, a set of 500 images is randomly extracted 
+from the database taken from the BOSS contest~\cite{Boss10}. 
+In this set, each cover is a $512\times 512$
+grayscale digital image.
+
+
+\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.
+In this strategy, two methods are thus applied to extract bits that 
+are modified. The first one is a direct application of the STC algorithm.
+This method is further referred as \emph{adaptive+STC}.
+The second one randomly choose the subset of pixels to modify by 
+applying the BBS PRNG again. This method is denoted \emph{adaptive+sample}.
+Notice that the rate between 
+available bits  and bit message length is always equal to two.
+This constraint is indeed induced by the fact that the efficiency 
+of the STC algorithm is unsatisfactory under that threshold.
+
+On our experiments and with the adaptive scheme, 
+the average size of the message that can be embedded is 16445.
+Its corresponds to an  average payload of 6.35\%. 
+
+
+
+
+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 again randomly chosen with BBS.
+
+ 
 
 \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.
 
 \begin{table}
 \begin{center}
-\begin{tabular}{|c|c|c|}
+\begin{tabular}{|c|c|c||c|c|}
+\hline
+Schemes & \multicolumn{3}{|c|}{STABYLO} & HUGO\\
+\hline
+Embedding &   \multicolumn{2}{|c||}{Adaptive} & Fixed & Fixed \\
 \hline
-Embedding rate &  Adaptive 
-10 \% &  \\
+Rate &   + STC &  + sample & 10\% & 10\%\\ 
 \hline
-PSNR &      &      \\
+PSNR &  66.55 & 63.48  & 61.86  & 64.65   \\ 
 \hline
-PSNR-HVS-M & 78.6  & 72.9 \\
+PSNR-HVS-M & 78.6 & 75.39 & 72.9 & 76.67\\ 
 \hline
-BIQI & 28.3 & 28.4 \\
+BIQI & 28.3 & 28.28 & 28.4 & 28.28\\ 
 \hline
-wPSNR & 86.43& 77.47 \\
+wPSNR & 86.43& 80.59 & 77.47& 83.03\\ 
 \hline
 \end{tabular}
 \end{center}
-\caption{Quality measeures of our steganography approach\label{table:quality}} 
+\caption{Quality Measures of Steganography Approaches\label{table:quality}} 
 \end{table}
 
+Let us give an interpretation of these experiments.
+First of all, the adaptive strategy produces images with lower distortion 
+than the one of images resulting from the 10\% fixed strategy.
+Numerical results are indeed always greater for the former strategy than 
+for the latter, except for the BIQI metrics where differences are not relevant.
+These results are not surprising since the adaptive strategy aims at 
+embedding messages whose length is decided according to a higher threshold
+into the edge detection.  
+Let us focus on the quality of HUGO images: with a given fixed 
+embedding rate (10\%) 
+HUGO always produces images whose quality is higher than the STABYLO's one.
+However, our approach nevertheless provides better results with the strategy 
+adaptive+STC in a lightweight manner, as motivated in the introduction.      
+
+
+Let us now compare the STABYLO approach with other edge based steganography
+schemes with respect to the image quality.
+First of all, the Edge Adaptive
+scheme detailed in~\cite{Luo:2010:EAI:1824719.1824720} 
+executed with a 10\% embedding rate 
+has the same PSNR but a lower wPSNR than our:
+these two metrics are respectively equal to 61.9 and 68.9. 
+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.
 
-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}
 
 
 \subsection{Steganalysis}
@@ -58,14 +129,17 @@ 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\\
+\begin{tabular}{|c|c|c|c|c|}
 \hline
-Embedding rate &  Adaptive & 10 \% &  10 \%\\
+Schemes & \multicolumn{3}{|c|}{STABYLO} & HUGO\\
 \hline
-AUMP & 0.39  & 0.22     &  0.50     \\
+Embedding &   \multicolumn{2}{|c|}{Adaptive} & Fixed & Fixed \\
 \hline
-Ensemble Classifier &   &      &      \\
+Rate &   + STC &  + sample & 10\% & 10\%\\ 
+\hline
+AUMP & 0.39  & 0.33  & 0.22     &  0.50     \\
+\hline
+Ensemble Classifier & 0.47 & 0.44 & 0.35     & 0.48     \\
 
 \hline
 \end{tabular}
@@ -74,4 +148,7 @@ Ensemble Classifier &   &      &      \\
 \end{table}
 
 
-\JFC{Raphael, il faut donner des rÃ©sultats ici}
\ No newline at end of file
+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.   
+
