X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/f919ede766f1129e330f7d12d04ed3e8ca8111b0..c6d3dd0e0b8e5c982b6a8d28649ab4079666815d:/experiments.tex?ds=sidebyside

diff --git a/experiments.tex b/experiments.tex
index a2126d3..8b674e0 100644
--- 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.