<html><head><meta name="color-scheme" content="light dark"></head><body><pre style="word-wrap: break-word; white-space: pre-wrap;">X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/3d3a92a59f998403d73c474b6565a18b3c248dec..b04e55fbbcdcbca04098f1bf05ec4473e47b5c51:/experiments.tex?ds=inline

diff --git a/experiments.tex b/experiments.tex
index 3babfb3..6660072 100644
--- a/experiments.tex
+++ b/experiments.tex
@@ -1,20 +1,20 @@
-For whole experiments, the whole set of 10,000 images 
+For all the experiments, the whole set of 10,000 images 
 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 focused on 
-the methodology than on benchmarking.    
+the methodology than on benchmarks.    
 
 We use the matrices $\hat{H}$ 
 generated by the integers given
-in table~\ref{table:matrices:H}
+in Table~\ref{table:matrices:H}
 as introduced in~\cite{FillerJF11}, since these ones have experimentally 
 be proven to have the best modification efficiency.
 For instance if the rate between the size of the message and the size of the 
 cover vector
 is 1/4, each number in $\{81, 95, 107, 121\}$ is translated into a binary number 
-and each one consitutes thus a column of $\hat{H}$. 
+and each one constitutes thus a column of $\hat{H}$. 
 
 \begin{table}
 $$
@@ -85,24 +85,25 @@ If $b$ is 6, these values are respectively equal to
 
 \begin{table*}
 \begin{center}
-\begin{tabular}{|c|c|c||c|c|c|c|c|c|}
+\begin{small}
+\begin{tabular}{|c|c|c||c|c|c|c|c|c|c|c|c|c|}
 \hline
-Schemes &amp; \multicolumn{4}{|c|}{STABYLO} &amp; \multicolumn{2}{|c|}{HUGO}&amp; \multicolumn{2}{|c|}{EAISLSBMR} \\
+Schemes &amp; \multicolumn{4}{|c|}{STABYLO} &amp; \multicolumn{2}{|c|}{HUGO}&amp; \multicolumn{2}{|c|}{EAISLSBMR} &amp; \multicolumn{2}{|c|}{WOW} &amp; \multicolumn{2}{|c|}{UNIWARD}\\
 \hline
-Embedding &amp;   Fixed &amp; \multicolumn{3}{|c|}{Adaptive (about 6.35\%)} &amp;  \multicolumn{2}{|c|}{Fixed}&amp; \multicolumn{2}{|c|}{Fixed} \\
+Embedding &amp;   Fixed &amp; \multicolumn{3}{|c|}{Adaptive (about 6.35\%)} &amp;  Fixed &amp;Adaptive &amp; Fixed &amp;Adaptive &amp; Fixed &amp;Adaptive &amp; Fixed &amp;Adaptive \\
 \hline
-Rate &amp;   10\% &amp;  + sample &amp;  +STC(7) &amp; +STC(6) &amp;  10\%&amp;6.35\%&amp; 10\%&amp;6.35\%\\ 
+Rate &amp;   10\% &amp;  + sample &amp;  +STC(7) &amp; +STC(6) &amp;  10\%&amp;6.35\%&amp; 10\%&amp;6.35\%&amp; 10\%&amp;6.35\%&amp; 10\%&amp;6.35\%\\ 
 \hline
-PSNR &amp; 61.86 &amp; 63.48 &amp;  66.55 (\textbf{-0.8\%}) &amp;  63.7  &amp; 64.65 &amp; {67.08} &amp; 60.8 &amp; 62.9\\ 
+PSNR &amp; 61.86 &amp; 63.48 &amp;  66.55 (\textbf{-0.8\%}) &amp;  63.7  &amp; 64.65 &amp; {67.08} &amp; 60.8 &amp; 62.9&amp;65.9 &amp; 68.3 &amp; 65.8 &amp; 69.2\\ 
 \hline
 PSNR-HVS-M &amp; 72.9 &amp; 75.39 &amp; 78.6 (\textbf{-0.8\%}) &amp; 75.5  &amp; 76.67 &amp; {79.23} &amp; 71.8  &amp; 74.3\\ 
 %\hline
 %BIQI &amp; 28.3 &amp; 28.28 &amp; 28.4 &amp; 28.28 &amp; 28.28 &amp; 28.2 &amp; 28.2\\ 
 \hline
-wPSNR &amp; 77.47 &amp; 80.59 &amp; 86.43(\textbf{-1.6\%})&amp; 86.28  &amp; 83.03 &amp; {87.8} &amp; 76.7 &amp; 80.6\\ 
+wPSNR &amp; 77.47 &amp; 80.59 &amp; 86.43(\textbf{-1.6\%})&amp; 86.28  &amp; 83.03 &amp; {88.6} &amp; 76.7 &amp; 83&amp; 83.8 &amp; 90.4 &amp; 85.2 &amp; 91.9\\ 
 \hline
 \end{tabular}
-
+\end{small}
 \begin{footnotesize}
 \vspace{2em}
 Variances given in bold font express the quality differences between 
@@ -141,7 +142,7 @@ in STC(6), data are hidden in the last two  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:
-providing an efficient steganography approach with a lightweight manner.
+providing an efficient steganography approach in a lightweight manner.
 
 
 Let us now compare the STABYLO approach with other edge based steganography
@@ -177,22 +178,22 @@ considered as state of the art steganalysers in the spatial domain~\cite{FK12}.
 
 \begin{table*}
 \begin{center}
-%\begin{small}
-\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
+\begin{small}
+\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|}
 \hline
-Schemes &amp; \multicolumn{4}{|c|}{STABYLO} &amp; \multicolumn{2}{|c|}{HUGO}&amp; \multicolumn{2}{|c|}{EAISLSBMR}\\
+Schemes &amp; \multicolumn{4}{|c|}{STABYLO} &amp; \multicolumn{2}{|c|}{HUGO}&amp; \multicolumn{2}{|c|}{EAISLSBMR} &amp;  \multicolumn{2}{|c|}{WOW} &amp;  \multicolumn{2}{|c|}{UNIWARD}\\
 \hline
-Embedding &amp; Fixed &amp;   \multicolumn{3}{|c|}{Adaptive (about 6.35\%)}  &amp; \multicolumn{2}{|c|}{Fixed}&amp; \multicolumn{2}{|c|}{Fixed} \\
+Embedding &amp; Fixed &amp;   \multicolumn{3}{|c|}{Adaptive (about 6.35\%)}  &amp; Fixed &amp; Adapt. &amp; Fixed &amp; Adapt. &amp; Fixed &amp; Adapt. &amp; Fixed &amp; Adapt. \\
 \hline
-Rate &amp; 10\% &amp;  + sample &amp;   +STC(7) &amp; +STC(6)   &amp; 10\%&amp; 6.35\%&amp; 10\%&amp; 6.35\%\\ 
+Rate &amp; 10\% &amp;  + sample &amp;   +STC(7) &amp; +STC(6)   &amp; 10\%&amp; 6.35\%&amp; 10\%&amp; 6.35\% &amp; 10\%&amp; 6.35\%&amp; 10\%&amp; 6.35\%\\ 
 \hline
 %AUMP &amp; 0.22 &amp; 0.33 &amp; 0.39  &amp;   0.45    &amp;  0.50 &amp;  0.50 &amp; 0.49 &amp; 0.50 \\
 %\hline
-Ensemble Classifier &amp; 0.35 &amp; 0.44 &amp; 0.47 &amp; 0.47     &amp; 0.48 &amp;  0.49  &amp;  0.43  &amp; 0.46 \\
+Ensemble Classifier &amp; 0.35 &amp; 0.44 &amp; 0.47 &amp; 0.47     &amp; 0.48 &amp;  0.49  &amp;  0.43  &amp; 0.47 &amp; 0.48 &amp; 0.49 &amp; 0.46 &amp; 0.49 \\
 
 \hline
 \end{tabular}
-%\end{small}
+\end{small}
 \end{center}
 \caption{Steganalysing STABYLO\label{table:steganalyse}} 
 \end{table*}
@@ -215,4 +216,3 @@ Compared to EAILSBMR, we obtain better results when the strategy is
 However due to its 
 huge number of integration features, it is not lightweight, which justifies 
 in the authors' opinion the consideration of the proposed method.   
-
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