X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/53495cfa81215f89ff4e577247fda977b29ed265..16874923aed7a3bc8380a6041e5c6b3d82f5683c:/experiments.tex?ds=inline

diff --git a/experiments.tex b/experiments.tex
index 710164c..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.
 
 
 
@@ -148,7 +148,7 @@ Ensemble Classifier & 0.47 & 0.44 & 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.