X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/6ef43069deff24f51ebf42e0c86c755c2b056c92..5178f772a789c49b261c48acc466a982d413a9a9:/experiments.tex?ds=inline

diff --git a/experiments.tex b/experiments.tex
index 245a461..9590b1c 100644
--- a/experiments.tex
+++ b/experiments.tex
@@ -216,3 +216,13 @@ However due to its huge number of integration features, it is not lightweight.
 All these numerical experiments confirm 
 the objective presented in the motivations:
 providing an efficient steganography approach in a lightweight manner.
+
+\RC{In Figure~\ref{fig:error}, Ensemble Classifier has been used with all the previsou steganalizers with 3 different payloads. It can be observed that with important payload, STABYLO is not efficient, but as mentionned its complexity is far more simple compared to other tools.\\
+\begin{figure}
+\begin{center}
+\includegraphics[scale=0.5]{error}
+\end{center}
+\caption{Error obtained by Ensemble classifier with WOW/UNIWARD, HUGO, and STABYLO and different paylaods.}
+\label{fig:error} 
+\end{figure}
+}