gfdg

[canny.git] / experiments.tex

diff --git a/experiments.tex b/experiments.tex
index a2126d3e108ef36dc1c25bb359cc93df75ccc3f1..8b674e0d78c61e7f11707fd7942df623fb4f4492 100644 (file)
--- a/experiments.tex
+++ b/experiments.tex
@@ -123,7 +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.

 
 
 
 
 
 


@@ -137,9 +137,9 @@ Embedding &   \multicolumn{2}{|c|}{Adaptive} & Fixed & Fixed \\
 \hline
 Rate &   + STC &  + sample & 10\% & 10\%\\ 
 \hline
 \hline
 Rate &   + STC &  + sample & 10\% & 10\%\\ 
 \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}


@@ -148,7 +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.