ajout de complexite

[canny.git] / experiments.tex

diff --git a/experiments.tex b/experiments.tex
index 2fc8a68527d8156735290d45b82898090e7d4289..9af03c672885bd3557429e9dd2cc5f88c3dc0af5 100644 (file)
--- a/experiments.tex
+++ b/experiments.tex
@@ -1,4 +1,4 @@
-For whole experiments, the whole set of 10000 images 
+For whole 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. 
 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. 


@@ -8,7 +8,7 @@ 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 least detectable information hiding tool in spatial domain 
 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 least detectable information hiding tool in spatial domain 

-and the later is the work that is close to ours, as far as we know. 
+and the latter is the work that is the closest to ours, as far as we know. 

 
 
 
 
 
 


@@ -16,7 +16,7 @@ First of all,  in our experiments and with the adaptive scheme,
 the average size of the message that can be embedded is 16,445 bits.
 Its corresponds to an  average payload of 6.35\%. 
 The two other tools will then be compared with this payload. 
 the average size of the message that can be embedded is 16,445 bits.
 Its corresponds to an  average payload of 6.35\%. 
 The two other tools will then be compared with this payload. 

-The Sections~\ref{sub:quality} and~\ref{sub:steg} respectively present 
+Sections~\ref{sub:quality} and~\ref{sub:steg} respectively present 

 the quality analysis and the security of our scheme. 
 
 
 the quality analysis and the security of our scheme. 
 
 


@@ -50,7 +50,7 @@ If $b$ is 6, these values are respectively equal to
 \hline
 Schemes & \multicolumn{4}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR} \\
 \hline
 \hline
 Schemes & \multicolumn{4}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR} \\
 \hline

-Embedding &   Fixed & \multicolumn{3}{|c|}{Adaptive} &  \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
+Embedding &   Fixed & \multicolumn{3}{|c|}{Adaptive (about 6.35\%)} &  \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\

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


@@ -79,7 +79,7 @@ HUGO and STABYLO with  STC+adaptive parameters.
 Results are summarized in Table~\ref{table:quality}.
 Let us give an interpretation of these experiments.
 First of all, the adaptive strategy produces images with lower distortion 
 Results are summarized in Table~\ref{table:quality}.
 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.
+than the images resulting from the 10\% fixed strategy.

 Numerical results are indeed always greater for the former strategy than 
 for the latter one.
 These results are not surprising since the adaptive strategy aims at 
 Numerical results are indeed always greater for the former strategy than 
 for the latter one.
 These results are not surprising since the adaptive strategy aims at 


@@ -88,12 +88,12 @@ 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.
 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 always outperforms EAISLSBMR since this one may modify 
-the two least significant bits whereas STABYLO only alter LSB.
+However our approach is always better than EAISLSBMR since this one may modify 
+the two least significant bits.

 
 If we combine \emph{adaptive} and \emph{STC} strategies 
 (which leads to an average embedding rate equal to 6.35\%)
 
 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.
+our approach  provides metrics equivalent to those provided by HUGO.

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


@@ -122,7 +122,7 @@ 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 aim at detecting hidden bits in grayscale natural images and are 
 AUMP~\cite{Fillatre:2012:ASL:2333143.2333587}
 and Ensemble Classifier~\cite{DBLP:journals/tifs/KodovskyFH12} based steganalysers.
 Both aim at detecting hidden bits in grayscale natural images and are 

-considered as the state of the art of steganalysers in spatial domain~\cite{FK12}.
+considered as state of the art steganalysers in the spatial domain~\cite{FK12}.

 The former approach is based on a simplified parametric model of natural images.
 Parameters are firstly estimated and an adaptive Asymptotically Uniformly Most Powerful
 (AUMP) test is designed (theoretically and practically), to check whether
 The former approach is based on a simplified parametric model of natural images.
 Parameters are firstly estimated and an adaptive Asymptotically Uniformly Most Powerful
 (AUMP) test is designed (theoretically and practically), to check whether


@@ -141,7 +141,7 @@ can be favorably executed thanks to an ensemble classifier.
 \hline
 Schemes & \multicolumn{4}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR}\\
 \hline
 \hline
 Schemes & \multicolumn{4}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR}\\
 \hline

-Embedding & Fixed &   \multicolumn{3}{|c|}{Adaptive}  & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
+Embedding & Fixed &   \multicolumn{3}{|c|}{Adaptive (about 6.35\%)}  & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\

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


@@ -163,9 +163,9 @@ 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
 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.
+our approach obtains similar results to HUGO ones.

 
 However due to its 
 
 However due to its 

-huge number of features integration, it is not lightweight, which justifies 
+huge number of integration features, it is not lightweight, which justifies 

 in the authors' opinion the consideration of the proposed method.   
 
 in the authors' opinion the consideration of the proposed method.