gfdg

[canny.git] / experiments.tex

diff --git a/experiments.tex b/experiments.tex
index 60cce0baf515c9c50f2f9568152dffcf568db2ab..8b674e0d78c61e7f11707fd7942df623fb4f4492 100644 (file)
--- a/experiments.tex
+++ b/experiments.tex
@@ -16,7 +16,7 @@ Canny algorithm with high threshold.
 The message length is thus defined to be the half of this set cardinality.
 In this strategy, two methods are thus applied to extract bits that 
 are modified. The first one is a direct application of the STC algorithm.
 The message length is thus defined to be the half of this set cardinality.
 In this strategy, two methods are thus applied to extract bits that 
 are modified. The first one is a direct application of the STC algorithm.

-This method is further refered as \emph{adaptive+STC}.
+This method is further referred as \emph{adaptive+STC}.

 The second one randomly choose the subset of pixels to modify by 
 applying the BBS PRNG again. This method is denoted \emph{adaptive+sample}.
 Notice that the rate between 
 The second one randomly choose the subset of pixels to modify by 
 applying the BBS PRNG again. This method is denoted \emph{adaptive+sample}.
 Notice that the rate between 


@@ -57,8 +57,11 @@ The other last ones have been designed to tackle this problem.
 \begin{center}
 \begin{tabular}{|c|c|c||c|c|}
 \hline
 \begin{center}
 \begin{tabular}{|c|c|c||c|c|}
 \hline

- &   \multicolumn{2}{|c||}{Adaptive} & fixed & HUGO \\
-Embedding rate &   + STC &  + sample & 10\% & 10\%\\ 
+Schemes & \multicolumn{3}{|c|}{STABYLO} & HUGO\\
+\hline
+Embedding &   \multicolumn{2}{|c||}{Adaptive} & Fixed & Fixed \\
+\hline
+Rate &   + STC &  + sample & 10\% & 10\%\\ 

 \hline
 PSNR &  66.55 & 63.48  & 61.86  & 64.65   \\ 
 \hline
 \hline
 PSNR &  66.55 & 63.48  & 61.86  & 64.65   \\ 
 \hline


@@ -70,21 +73,21 @@ wPSNR & 86.43& 80.59 & 77.47& 83.03\\
 \hline
 \end{tabular}
 \end{center}
 \hline
 \end{tabular}
 \end{center}

-\caption{Quality measures of our steganography approach\label{table:quality}} 
+\caption{Quality Measures of Steganography Approaches\label{table:quality}} 

 \end{table}
 
 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.
 Numerical results are indeed always greater for the former strategy than 
 \end{table}
 
 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.
 Numerical results are indeed always greater for the former strategy than 

-for the latter, except for the BIQI metrics where differences are not relevent.
+for the latter, except for the BIQI metrics where differences are not relevant.

 These results are not surprising since the adaptive strategy aims at 
 embedding messages whose length is decided according to a higher threshold
 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.
 These results are not surprising since the adaptive strategy aims at 
 embedding messages whose length is decided according to a higher threshold
 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 approach nevertheless provides beter results with the strategy 
+However, our approach nevertheless provides better results with the strategy 

 adaptive+STC in a lightweight manner, as motivated in the introduction.      
 
 
 adaptive+STC in a lightweight manner, as motivated in the introduction.      
 
 


@@ -120,7 +123,7 @@ a natural image has stego content or not.
 In the latter, the authors show that the 
 machine learning step, (which is often
 implemented as support vector machine)
 In the latter, the authors show that the 
 machine learning step, (which is often
 implemented as support vector machine)

-can be a favourably executed thanks to an Ensemble Classifiers.
+can be a favorably executed thanks to an Ensemble Classifiers.

 
 
 
 
 
 


@@ -130,13 +133,13 @@ can be a favourably executed thanks to an Ensemble Classifiers.
 \hline
 Schemes & \multicolumn{3}{|c|}{STABYLO} & HUGO\\
 \hline
 \hline
 Schemes & \multicolumn{3}{|c|}{STABYLO} & HUGO\\
 \hline

-Embedding rate &  \multicolumn{2}{|c|}{Adaptive} & 10 \% &  10 \%\\
- &   + STC &  + sample &  & \\ 
-
+Embedding &   \multicolumn{2}{|c|}{Adaptive} & Fixed & Fixed \\
+\hline
+Rate &   + STC &  + sample & 10\% & 10\%\\ 

 \hline
 \hline

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

 \hline
 \hline

-Ensemble Classifier & 0.47 &  & 0.35     & 0.48     \\
+Ensemble Classifier & 0.47 & 0.44 & 0.35     & 0.48     \\

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


@@ -145,7 +148,7 @@ Ensemble Classifier & 0.47 &  & 0.35     & 0.48     \\
 \end{table}
 
 
 \end{table}
 
 

-Results show that our approach is more easily detectable than HUGO which is
-is the more secure steganography tool, as far we know. However due to its 
+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 

 huge number of features integration, it is not lightweight.   
 
 huge number of features integration, it is not lightweight.