X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/ded436ff1397fd455304cbae04f58e4c0703ea01..e109e0d899bc233ae4407afe3af6ca03e1afe90a:/experiments.tex

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 \subsection{Steganalysis}
 
 
-DÃ©tailler \cite{Fillatre:2012:ASL:2333143.2333587}
 
-Vainqueur du BOSS challenge~\cite{DBLP:journals/tifs/KodovskyFH12}
+The quality of our approach has been evaluated through the two 
+AUMP~\cite{Fillatre:2012:ASL:2333143.2333587}
+and Ensemble Classifier~\cite{DBLP:journals/tifs/KodovskyFH12} based steganalysers.
+Both aims at detecting hidden bits in grayscale natural images and are 
+considered as the state of the art of steganalysers in spatial domain~\cite{FK12}.
+The former approach is based on a simplified parametric model of natural images.
+Parameters are firstly estimated and a adaptive Asymptotically Uniformly Most Powerful
+(AUMP) test is designed (theoretically and practically) to check whether
+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 Classiï¬ers.
