-c% This file was created with JabRef 2.3.1.
+% This file was created with JabRef 2.3.1.
% Encoding: ANSI_X3.4-1968
+@article{LiFengyongZhang14,
+year={2014},
+issn={0003-4347},
+journal={annals of telecommunications - annales des télécommunications},
+volume={69},
+number={7-8},
+doi={10.1007/s12243-013-0415-2},
+title={Adaptive JPEG steganography with new distortion function},
+url={http://dx.doi.org/10.1007/s12243-013-0415-2},
+publisher={Springer Paris},
+keywords={Steganography; Distortion function; Steganalysis},
+author={Li, Fengyong and Zhang, Xinpeng and Yu, Jiang and Shen, Wenfeng},
+pages={431-440},
+language={English}
+}
+
+
@INPROCEEDINGS{nusmv02,
author = {Alessandro Cimatti and Edmund M. Clarke and Enrico Giunchiglia and
Fausto Giunchiglia and Marco Pistore and Marco Roveri and Roberto
ee = {http://dx.doi.org/10.1117/12.838768}
}
+
+
+@article{liu2014syndrome,
+ title={Syndrome trellis codes based on minimal span generator matrix},
+ author={Liu, Weiwei and Liu, Guangjie and Dai, Yuewei},
+ journal={annals of telecommunications-annales des t{\'e}l{\'e}communications},
+ volume={69},
+ number={7-8},
+ pages={403--416},
+ year={2014},
+ publisher={Springer}
+}
\ No newline at end of file
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.
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,
\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}
-}
pixels can be easily detected.
Practically speaking, if Alice would send
a hidden message to Bob, she would never consider
-such kind of image and a high embedding rate.
+such kind of image and a high embedding rate.
+\JFC{This desire to be adaptive has been
+studied too in~\cite{LiFengyongZhang14},
+but in JPEG frequency domain}.
The approach we propose here is thus to provide a small complexity
self adaptive algorithm
with an acceptable payload, which
-\newcommand{\JFC}[1]{\begin{color}{green}\textit{#1}\end{color}}
+\newcommand{\JFC}[1]{\begin{color}{red}\textit{#1}\end{color}}
\newcommand{\RC}[1]{\begin{color}{red}\textit{#1}\end{color}}
-\newcommand{\CG}[1]{\begin{color}{blue}\textit{#1}\end{color}}
+\newcommand{\CG}[1]{\begin{color}{red}\textit{#1}\end{color}}
% make the title area
systematic replacement of all the LSBs of edges by binary digits provided
by the BBS generator will be investigated, and the consequences of such a
replacement, in terms of security, will be discussed.
-Furthermore, we plan to investigate information hiding on other models, such as high frequency for JPEG encoding.
+Furthermore, we plan to investigate information hiding on other models,
+such as high frequency for JPEG encoding.
%\bibliographystyle{spbasic}
\subsection{Security considerations}\label{sub:bbs}
-\JFC{To provide a self-contained article without any bias, we shor\-tly
-present the selected encryption process.}
+To provide a self-contained article without any bias, we shor\-tly
+present the selected 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).
-\JFC{A reader who is familar with syndrome coding can skip it.}
+A reader who 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 $X$,
Moreover, for any given $H$, finding $y$ that solves $Hy=m$ and
that minimizes $D_X(x,y)$, has an exponential complexity with respect to $n$.
The Syndrome-Trellis Codes
-presented by Filler \emph{et al.} in~\cite{FillerJF11}
+presented by Filler \emph{et al.} \JFC{in~\cite{FillerJF11,liu2014syndrome}}
is a practical solution to this complexity. Thanks to this contribution,
the solving algorithm has a linear complexity with respect to $n$.
