fin des exp

[canny.git] / experiments.tex

diff --git a/experiments.tex b/experiments.tex
index 9da4704046665ae6980ba9ba7ff4a47273f45ba0..9c7302585af6241dcdcd94d1b640cb00b2bd7d4f 100644 (file)
--- a/experiments.tex
+++ b/experiments.tex
@@ -7,7 +7,7 @@ this set of cover images since this paper is more focused on
 the methodology than benchmarking.    
 Our approach is always compared to Hugo~\cite{DBLP:conf/ih/PevnyFB10}
 and to EAISLSBMR~\cite{Luo:2010:EAI:1824719.1824720}.
 the methodology than benchmarking.    
 Our approach is always compared to Hugo~\cite{DBLP:conf/ih/PevnyFB10}
 and to EAISLSBMR~\cite{Luo:2010:EAI:1824719.1824720}.

-The former is the less detectable information hidding tool in spatial domain 
+The former is the less detectable information hiding tool in spatial domain 

 and the later is the work which is close to ours, as far as we know. 
 
 
 and the later is the work which is close to ours, as far as we know. 
 
 


@@ -35,26 +35,32 @@ The first one is widely used but does not take into
 account the Human Visual System (HVS).
 The other ones have been designed to tackle this problem.
 
 account the Human Visual System (HVS).
 The other ones have been designed to tackle this problem.
 

+If we apply them on the running example, 
+the PSNR, PSNR-HVS-M, and wPSNR values are respectively equal to 
+68.39, 79.85, and 89.71 for the stego Lena when $b$ is equal to 7.
+If $b$ is 6, these values are respectively equal to 
+65.43, 77.2, and 89.35.
+

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

-\begin{tabular}{|c|c|c||c|c|c|c|c|}
+\begin{tabular}{|c|c|c||c|c|c|c|c|c|}

 \hline
 \hline

-Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR} \\
+Schemes & \multicolumn{4}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR} \\

 \hline
 \hline

-Embedding &   Fixed & \multicolumn{2}{|c|}{Adaptive} &  \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
+Embedding &   Fixed & \multicolumn{3}{|c|}{Adaptive} &  \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\

 \hline
 \hline

-Rate &   10\% &  + sample & + STC  &  10\%&6.35\%& 10\%&6.35\%\\ 
+Rate &   10\% &  + sample &  +STC(7) & +STC(6) &  10\%&6.35\%& 10\%&6.35\%\\ 

 \hline
 \hline

-PSNR & 61.86 & 63.48 &  66.55 (\textbf{-0.8\%})     & 64.65 & {67.08} & 60.8 & 62.9\\ 
+PSNR & 61.86 & 63.48 &  66.55 (\textbf{-0.8\%}) &  63.7  & 64.65 & {67.08} & 60.8 & 62.9\\ 

 \hline
 \hline

-PSNR-HVS-M & 72.9 & 75.39 & 78.6 (\textbf{-0.8\%})    & 76.67 & {79.23} & 61.3  & 63.4\\ 
+PSNR-HVS-M & 72.9 & 75.39 & 78.6 (\textbf{-0.8\%}) & 75.5  & 76.67 & {79.23} & 71.8  & 74.3\\ 

 %\hline
 %BIQI & 28.3 & 28.28 & 28.4 & 28.28 & 28.28 & 28.2 & 28.2\\ 
 \hline
 %\hline
 %BIQI & 28.3 & 28.28 & 28.4 & 28.28 & 28.28 & 28.2 & 28.2\\ 
 \hline

-wPSNR & 77.47 & 80.59 & 86.43(\textbf{-1.6\%})  & 83.03 & {87.8} & 76.7 & 80.6\\ 
+wPSNR & 77.47 & 80.59 & 86.43(\textbf{-1.6\%})& 86.28  & 83.03 & {87.8} & 76.7 & 80.6\\ 

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


@@ -88,6 +94,11 @@ the two least significant bits whereas STABYLO only alter LSB.
 If we combine \emph{adaptive} and \emph{STC} strategies 
 (which leads to an average embedding rate equal to 6.35\%)
 our approach  provides equivalent metrics than HUGO.
 If we combine \emph{adaptive} and \emph{STC} strategies 
 (which leads to an average embedding rate equal to 6.35\%)
 our approach  provides equivalent metrics than HUGO.

+In this column STC(7) stands for embeding data in the LSB whereas
+in STC(6), data are hidden in the two last significant bits. 
+
+
+

 The quality variance between HUGO and STABYLO for these parameters
 is given in bold font. It is always close to 1\% which confirms 
 the objective presented in the motivations:
 The quality variance between HUGO and STABYLO for these parameters
 is given in bold font. It is always close to 1\% which confirms 
 the objective presented in the motivations:


@@ -126,17 +137,17 @@ can be favorably executed thanks to an ensemble classifier.
 \begin{table*}
 \begin{center}
 %\begin{small}
 \begin{table*}
 \begin{center}
 %\begin{small}

-\begin{tabular}{|c|c|c|c|c|c|c|c|}
+\begin{tabular}{|c|c|c|c|c|c|c|c|c|}

 \hline
 \hline

-Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR}\\
+Schemes & \multicolumn{4}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR}\\

 \hline
 \hline

-Embedding & Fixed &   \multicolumn{2}{|c|}{Adaptive}  & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
+Embedding & Fixed &   \multicolumn{3}{|c|}{Adaptive}  & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\

 \hline
 \hline

-Rate & 10\% &  + sample &   + STC   & 10\%& 6.35\%& 10\%& 6.35\%\\ 
+Rate & 10\% &  + sample &   +STC(7) & +STC(6)   & 10\%& 6.35\%& 10\%& 6.35\%\\ 

 \hline
 \hline

-AUMP & 0.22 & 0.33 & 0.39         &  0.50 &  0.50 & 0.49 & 0.50 \\
+AUMP & 0.22 & 0.33 & 0.39  &   0.45    &  0.50 &  0.50 & 0.49 & 0.50 \\

 \hline
 \hline

-Ensemble Classifier & 0.35 & 0.44 & 0.47       & 0.48 &  0.49  &  0.43  & 0.46 \\
+Ensemble Classifier & 0.35 & 0.44 & 0.47 & 0.47     & 0.48 &  0.49  &  0.43  & 0.46 \\

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


@@ -153,6 +164,7 @@ Next, our approach is more easily detectable than HUGO, which
 is the most secure steganographic tool, as far as we know. 
 However by combining \emph{adaptive} and \emph{STC} strategies
 our approach obtains similar results than HUGO ones.
 is the most secure steganographic tool, as far as we know. 
 However by combining \emph{adaptive} and \emph{STC} strategies
 our approach obtains similar results than HUGO ones.

+

 However due to its 
 huge number of features integration, it is not lightweight, which justifies 
 in the authors' opinion the consideration of the proposed method.   
 However due to its 
 huge number of features integration, it is not lightweight, which justifies 
 in the authors' opinion the consideration of the proposed method.