<html><head><meta name="color-scheme" content="light dark"></head><body><pre style="word-wrap: break-word; white-space: pre-wrap;">X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/9c37522d37b3caa216649cc98987e4c89b42091f..ff62ab9a78e83759f5e40e3b7d422fddeb45115d:/experiments.tex?ds=inline

diff --git a/experiments.tex b/experiments.tex
index 61d2971..6d65b81 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}
+}
</pre></body></html>