}
+
+
+@inproceedings{DBLP:dblp_conf/mediaforensics/KodovskyPF10,
+ author = {Jan Kodovský and
+ Tomás Pevný and
+ Jessica J. Fridrich},
+ title = {Modern steganalysis can detect YASS.},
+ booktitle = {Media Forensics and Security},
+ year = {2010},
+ pages = {754102},
+ ee = {http://dx.doi.org/10.1117/12.838768}
+}
+
Ensemble Classifier~\cite{DBLP:journals/tifs/KodovskyFH12} based steganalyser.
Its particularization to spatial domain is
considered as state of the art steganalysers.
-Firstly, a space
-of 686 co-occurrence and Markov features is extracted from the
-set of cover images and the set of training images. Next a small
+\JFC{Features that are embedded into this steganalysis process
+are CCPEV and SPAM features as described
+in~\cite{DBLP:dblp_conf/mediaforensics/KodovskyPF10}.
+These one are extracted from the
+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.
The final classifier is constructed by a majority voting
\subsection{Security considerations}\label{sub:bbs}
+\JFC{To provide a self-contained article without any bias, we shortly
+pressent the retained encryption process.}
Among the methods of message encryption/decryption
(see~\cite{DBLP:journals/ejisec/FontaineG07} for a survey)
we implement the asymmetric
To make this article self-contained, this section recalls
-the basis of the Syndrome Treillis Codes (STC).
+the basis of the Syndrome Treillis Codes (STC).
+\JFC{A reader that is familar with syndrome coding can skip it.}
Let
$x=(x_1,\ldots,x_n)$ be the $n$-bits cover vector issued from an image $X$,
