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[canny.git] / experiments.tex

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+++ b/experiments.tex
@@ -1,39 +1,109 @@
+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.
 
 \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.
 
 \begin{table}
 \begin{center}
 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
 \hline

-Embedding rate &  Adaptive 
-10 \% &  \\
+Rate &   + STC &  + sample & 10\% & 10\%\\ 

 \hline
 \hline

-PSNR &      &      \\
+PSNR &  66.55 & 63.48  & 61.86  & 64.65   \\ 

 \hline
 \hline

-PSNR-HVS-M & 78.6  & 72.9 \\
+PSNR-HVS-M & 78.6 & 75.39 & 72.9 & 76.67\\ 

 \hline
 \hline

-BIQI & 28.3 & 28.4 \\
+BIQI & 28.3 & 28.28 & 28.4 & 28.28\\ 

 \hline
 \hline

-wPSNR & 86.43& 77.47 \\
+wPSNR & 86.43& 80.59 & 77.47& 83.03\\ 

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

 
 
 
 
 
 


@@ -53,20 +123,23 @@ a natural image has stego content or not.
 In the latter, the authors show that the 
 machine learning step, (which is often
 implemented as support vector machine)
 In the latter, the authors show that the 
 machine learning step, (which is often
 implemented as support vector machine)

-can be a favourably executed thanks to an Ensemble Classifiers.
+can be a favorably executed thanks to an Ensemble Classifiers.

 
 
 
 \begin{table}
 \begin{center}
 
 
 
 \begin{table}
 \begin{center}

-\begin{tabular}{|c|c|c|c|}
-Shemes & \multicolumn{2}{|c|}{STABYLO} & HUGO\\
+\begin{tabular}{|c|c|c|c|c|}
+\hline
+Schemes & \multicolumn{3}{|c|}{STABYLO} & HUGO\\

 \hline
 \hline

-Embedding rate &  Adaptive & 10 \% &  10 \%\\
+Embedding &   \multicolumn{2}{|c|}{Adaptive} & Fixed & Fixed \\

 \hline
 \hline

-AUMP & 0.39  & 0.22     &  0.50     \\
+Rate &   + STC &  + sample & 10\% & 10\%\\ 

 \hline
 \hline

-Ensemble Classifier &   &      &      \\
+AUMP & 0.39  & 0.33  & 0.22     &  0.50     \\
+\hline
+Ensemble Classifier & 0.47 & 0.44 & 0.35     & 0.48     \\

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


@@ -75,3 +148,7 @@ Ensemble Classifier &   &      &      \\
 \end{table}
 
 
 \end{table}
 
 

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