X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/d5631915e956b49c89f78d66d7b4d25682a7e405..8a7410beb8e5821fa7764d6fdc6843effeed1e0e:/experiments.tex?ds=sidebyside

diff --git a/experiments.tex b/experiments.tex
index 15fb5c1..aa49e84 100644
--- a/experiments.tex
+++ b/experiments.tex
@@ -150,11 +150,11 @@ The steganalysis quality of our approach has been evaluated through the % two
 Ensemble Classifier~\cite{DBLP:journals/tifs/KodovskyFH12} based steganalyser.
 Its  particularization to spatial domain is 
 considered as state of the art steganalysers.
-\JFC{Features that are embedded into this steganalysis process 
+Features that are embedded into this steganalysis process 
 are CCPEV and SPAM features as described 
 in~\cite{DBLP:dblp_conf/mediaforensics/KodovskyPF10}.
 They  are extracted from the 
-set of cover images and the set of training images.}
+set of cover images and the set of training images.
 Next a small 
 set of weak classifiers is randomly built,
 each one working on a subspace of all the features.
@@ -218,7 +218,7 @@ the objective presented in the motivations:
 providing an efficient steganography approach in a lightweight manner
 for small payload.
 
-\RC{In Figure~\ref{fig:error}, 
+In Figure~\ref{fig:error}, 
 Ensemble Classifier has been used with all the previous 
 steganographic schemes with 4 different payloads.
 It can be observed that face to high values of payload, 
@@ -230,8 +230,7 @@ than a larger message in only one image.
 \begin{center}
 \includegraphics[scale=0.5]{error}
 \end{center}
-\caption{Testing error obtained by Ensemble classifier with 
+\caption{Testing errors obtained by Ensemble classifier with 
 WOW/UNIWARD, HUGO, and STABYLO w.r.t. payload.}
 \label{fig:error} 
 \end{figure}
-}