From: couchot Date: Tue, 25 Jun 2013 14:19:33 +0000 (+0200) Subject: fin des exp X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/commitdiff_plain/93faa138ef3fae7589a5dc3ba0d8862f7b54c4cb?ds=inline fin des exp --- diff --git a/experiments.tex b/experiments.tex index 8337d55..9c73025 100644 --- a/experiments.tex +++ b/experiments.tex @@ -46,21 +46,21 @@ If $b$ is 6, these values are respectively equal to \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 -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 -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 -Rate & 10\% & + sample & + STC & 10\%&6.35\%& 10\%&6.35\%\\ +Rate & 10\% & + sample & +STC(7) & +STC(6) & 10\%&6.35\%& 10\%&6.35\%\\ \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 -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 -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} @@ -94,6 +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. +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: @@ -132,17 +137,17 @@ can be favorably executed thanks to an ensemble classifier. \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 -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 -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 -Rate & 10\% & + sample & + STC & 10\%& 6.35\%& 10\%& 6.35\%\\ +Rate & 10\% & + sample & +STC(7) & +STC(6) & 10\%& 6.35\%& 10\%& 6.35\%\\ \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 -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} @@ -159,6 +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. + 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.