Relecture de l'intro

[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)
-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
-AUMP & 0.39  & & 0.22     &  0.50     \\
+AUMP & 0.39  & 0.33  & 0.22     &  0.50     \\
 \hline
-Ensemble Classifier & 0.47 &  & 0.35     & 0.48     \\
+Ensemble Classifier & 0.47 & 0.44 & 0.35     & 0.48     \\
 
 \hline
 \end{tabular}
@@ -148,7 +148,7 @@ Ensemble Classifier & 0.47 &  & 0.35     & 0.48     \\
 \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.