X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/3c3e9a12f31ac42d0c3e7eb1be25aeafb7f7b3db..b417a74f270da1ad1a8b11552a8508c45cb75085:/experiments.tex?ds=inline

diff --git a/experiments.tex b/experiments.tex
index aed0505..0c08d69 100644
--- a/experiments.tex
+++ b/experiments.tex
@@ -3,7 +3,7 @@ of the BOSS contest~\cite{Boss10} database is taken.
 In this set, each cover is a $512\times 512$
 grayscale digital image in a RAW format. 
 We restrict experiments to 
-this set of cover images since this paper is more focussed on 
+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}.
@@ -69,22 +69,28 @@ The other ones have been designed to tackle this problem.
 \hline
 Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR} \\
 \hline
-Embedding &   \multicolumn{2}{|c||}{Adaptive} & Fixed & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
+Embedding &   Fixed & \multicolumn{2}{|c|}{Adaptive} &  \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
 \hline
-Rate &   + STC &  + sample & 10\% & 10\%&6.35\%& 10\%&6.35\%\\ 
+Rate &   10\% &  + sample & + STC  &  10\%&6.35\%& 10\%&6.35\%\\ 
 \hline
-PSNR &  66.55 (\textbf{-0.8\%}) & 63.48  & 61.86  & 64.65 & {67.08} & 60.8 & 62.9\\ 
+PSNR & 61.86 & 63.48 &  66.55 (\textbf{-0.8\%})     & 64.65 & {67.08} & 60.8 & 62.9\\ 
 \hline
-PSNR-HVS-M & 78.6 (\textbf{-0.8\%})  & 75.39 & 72.9 & 76.67 & {79.23} & 61.3  & 63.4\\ 
+PSNR-HVS-M & 72.9 & 75.39 & 78.6 (\textbf{-0.8\%})    & 76.67 & {79.23} & 61.3  & 63.4\\ 
 %\hline
 %BIQI & 28.3 & 28.28 & 28.4 & 28.28 & 28.28 & 28.2 & 28.2\\ 
 \hline
-wPSNR & 86.43(\textbf{-1.6\%}) & 80.59 & 77.47& 83.03 & {87.8} & & 80.6\\ 
+wPSNR & 77.47 & 80.59 & 86.43(\textbf{-1.6\%})  & 83.03 & {87.8} & 76.7 & 80.6\\ 
 \hline
 \end{tabular}
+
+\begin{footnotesize}
+\vspace{2em}
+Variances given in bold font express the quality differences between 
+HUGO and STABYLO with  STC+adaptive parameters.
+\end{footnotesize}
+
 \end{center}
 \caption{Quality Measures of Steganography Approaches\label{table:quality}}
-\label{table:quality}
 \end{table*}
 
 
@@ -101,7 +107,7 @@ 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 appraoch always outperforms EAISLSBMR since this one may modify 
+However our approach always outperforms EAISLSBMR since this one may modify 
 the two least significant bits whereas STABYLO only alter LSB.
 
 If we combine \emph{adaptive} and \emph{STC} strategies 
@@ -126,7 +132,7 @@ give quality metrics for fixed embedding rates from a large base of images.
 
 
 
-The quality of our approach has been evaluated through the two 
+The steganalysis quality of our approach has been evaluated through the two 
 AUMP~\cite{Fillatre:2012:ASL:2333143.2333587}
 and Ensemble Classifier~\cite{DBLP:journals/tifs/KodovskyFH12} based steganalysers.
 Both aims at detecting hidden bits in grayscale natural images and are 
@@ -141,7 +147,6 @@ machine learning step, which is often
 implemented as support vector machine,
 can be favorably executed thanks to an ensemble classifier.
 
-%citer le second tableau, comparer avec EAISLSBMR
 
 \begin{table*}
 \begin{center}
@@ -150,13 +155,13 @@ can be favorably executed thanks to an ensemble classifier.
 \hline
 Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR}\\
 \hline
-Embedding &   \multicolumn{2}{|c|}{Adaptive} & Fixed & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
+Embedding & Fixed &   \multicolumn{2}{|c|}{Adaptive}  & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
 \hline
-Rate &   + STC &  + sample & 10\% & 10\%& 6.35\%& 10\%& 6.35\%\\ 
+Rate & 10\% &  + sample &   + STC   & 10\%& 6.35\%& 10\%& 6.35\%\\ 
 \hline
-AUMP & 0.39  & 0.33  & 0.22     &  0.50 &  0.50 & 0.49 & 0.50 \\
+AUMP & 0.22 & 0.33 & 0.39         &  0.50 &  0.50 & 0.49 & 0.50 \\
 \hline
-Ensemble Classifier & \textbf{0.47} & 0.44 & 0.35     & 0.48 &  0.49  &  0.43  & 0.46 \\
+Ensemble Classifier & 0.35 & 0.44 & 0.47       & 0.48 &  0.49  &  0.43  & 0.46 \\
 
 \hline
 \end{tabular}
@@ -166,8 +171,14 @@ Ensemble Classifier & \textbf{0.47} & 0.44 & 0.35     & 0.48 &  0.49  &  0.43  &
 \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 
+Results are summarized in Table~\ref{table:steganalyse}.
+First of all, STC outperforms the sample strategy for the two steganalysers, as 
+already noticed in the quality analysis presented in the previous section. 
+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.
+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.   
 
