\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
+These latter are extracted from the
set of cover images and the set of training images.}
Next a small
set of weak classifiers is randomly built,
We thus propose to take advantage of this optimized embedding, provided they are not too time consuming.
In the latter, an hybrid edge detector is presented followed by an ad hoc
embedding.
-The Edge detection is computed by combining fuzzy logic~\cite{Tyan1993}
+The Edge detection is computed by combining fuz\-zy logic~\cite{Tyan1993}
and Canny~\cite{Canny:1986:CAE:11274.11275} approaches.
The goal of this combination
is to enlarge the set of modified bits to increase the payload of the data hiding scheme.
long message in the cover signal.
Practically speaking, our approach is efficient enough for
payloads close to 0.06 bit per pixel which allows to embed
-messages of length larger than 15728 bits in an
+messages of length larger than 15,728 bits in an
image of size $512\times 512$ pixels.
% Message extraction is achieved by computing the same
To sum up, well-studied and experimented
techniques of signal processing (adaptive edges detection),
coding theory (syndrome-trellis codes), and cryptography
-(Blum-Goldwasser encryption protocol) are combined in this research work.
+(Blum-Goldwas\-ser encryption protocol) are combined in this research work.
The objective is to compute an efficient steganographic
-scheme, whose principal characteristic is to take into
+sche\-me, whose principal characteristic is to take into
consideration the cover image and to be compatible with small computation resources.
The remainder of this document is organized as follows.
the cover pixel selection (Sect.~\ref{sub:edge}),
the adaptive payload considerations (Sect.~\ref{sub:adaptive}),
and how the distortion has been minimized (Sect.~\ref{sub:stc}).
-The message extraction is then presented (Sect.~\ref{sub:extract}) while a running example ends this section (Sect.~\ref{sub:xpl}).
+The message extraction is then presented (Sect.~\ref{sub:extract}) while a running example ends this section.
The flowcharts given in Fig.~\ref{fig:sch}
\subsection{Security considerations}\label{sub:bbs}
-\JFC{To provide a self-contained article without any bias, we shortly
-pressent the retained encryption process.}
+\JFC{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
In this article, in the Adaptive strategy
we consider that all the edge pixels that
have been selected by this algorithm have the same
-distortion cost \textit{i.e.} $\rho_X$ is always 1 for these bits.
+distortion cost, \textit{i.e.}, $\rho_X$ is always 1 for these bits.
In the Fixed strategy, since pixels that are detected to be edge
-with small values of $T$ (e.g. when $T=3$)
+with small values of $T$ (e.g., when $T=3$)
are more accurate than these with higher values of $T$,
we give to STC the following distortion map of the corresponding bits
$$
\rho_X= \left\{
\begin{array}{l}
-1 \textrm{ if an edge for $T=3$} \\
-10 \textrm{ if an edge for $T=5$} \\
-100 \textrm{ if an edge for $T=7$}
+1 \textrm{ if an edge for $T=3$,} \\
+10 \textrm{ if an edge for $T=5$,} \\
+100 \textrm{ if an edge for $T=7$.}
\end{array}
\right.
-$$.
+$$
To make this article self-contained, this section recalls
the basis of the Syndrome Treillis Codes (STC).
-\JFC{A reader that is familar with syndrome coding can skip it.}
+\JFC{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 $X$,
