the methodology than benchmarking.
Our approach is always compared to Hugo~\cite{DBLP:conf/ih/PevnyFB10}
and to EAISLSBMR~\cite{Luo:2010:EAI:1824719.1824720}.
+The former is the less detectable information hidding tool in spatial domain
+and the later is the work which is close to ours, as far as we know.
-
-
-\subsection{Adaptive Embedding Rate}
-Two strategies have been developed in our scheme,
-depending on the embedding rate that is either \emph{adaptive} or \emph{fixed}.
-
-In the former the embedding rate depends on the number of edge pixels.
-The higher it is, the larger the message length that can be inserted is.
-Practically, a set of edge pixels is computed according to the
-Canny algorithm with an high threshold.
-The message length is thus defined to be half of this set cardinality.
-In this strategy, two methods are thus applied to extract bits that
-are modified. The first one is a direct application of the STC algorithm.
-This method is further referred to as \emph{adaptive+STC}.
-The second one randomly chooses the subset of pixels to modify by
-applying the BBS PRNG again. This method is denoted \emph{adaptive+sample}.
-Notice that the rate between
-available bits and bit message length is always equal to 2.
-This constraint is indeed induced by the fact that the efficiency
-of the STC algorithm is unsatisfactory under that threshold.
-In our experiments and with the adaptive scheme,
+First of all, in our experiments and with the adaptive scheme,
the average size of the message that can be embedded is 16,445 bits.
Its corresponds to an average payload of 6.35\%.
+The two other tools will then be compared with this payload.
+The Sections~\ref{sub:quality} and~\ref{sub:steg} respectively present
+the quality analysis and the security of our scheme.
-
-In the latter, the embedding rate is defined as a percentage between the
-number of modified pixels and the length of the bit message.
-This is the classical approach adopted in steganography.
-Practically, the Canny algorithm generates
-a set of edge pixels related to a threshold that is decreasing until its cardinality
-is sufficient. If the set cardinality is more than twice larger than the
-bit message length, a STC step is again applied.
-Otherwise, pixels are again randomly chosen with BBS.
-
-\subsection{Image Quality}
+\subsection{Image Quality}\label{sub:quality}
The visual quality of the STABYLO scheme is evaluated in this section.
-For the sake of completeness, four metrics are computed in these experiments:
+For the sake of completeness, three metrics are computed in these experiments:
the Peak Signal to Noise Ratio (PSNR),
the PSNR-HVS-M family~\cite{psnrhvsm11},
%the BIQI~\cite{MB10},
Results are summarized into the Table~\ref{table:quality}.
-Let us give an interpretation of these first experiments.
+Let us give an interpretation of these experiments.
First of all, the adaptive strategy produces images with lower distortion
than the one of images resulting from the 10\% fixed strategy.
Numerical results are indeed always greater for the former strategy than
-\subsection{Steganalysis}
+\subsection{Steganalysis}\label{sub:steg}
\usepackage{mathptmx,amsmath,amssymb,bm}
\usepackage{subfig}
\usepackage{color}
-
+\usepackage{mathtools,etoolbox}
\tolerance=1
\emergencystretch=\maxdimen
\def\theyear{2013}
-
+\DeclarePairedDelimiter{\abs}{\lvert}{\rvert}
% correct bad hyphenation here
\hyphenation{op-tical net-works semi-conduc-tor}
steganographic process in spatial domain.
Additionally to this effectiveness,
quite comparable results through noise measures like PSNR-HVS-M,
-BIQI, and weighted PSNR (wPSNR) are obtained.
+and weighted PSNR (wPSNR) are obtained.
To achieve the proposed goal, famous experimented
components of signal processing,
coding theory, and cryptography are combined together, leading to
For future work, the authors' intention is to investigate systematically
all the existing edge detection methods, to see if the STABYLO evaluation scores can
-be improved by replacing Canny with another edge filter. We will try
-to take into account the least significant bits too during all the
-stages of the algorithm, hoping by doing so to be closer to the HUGO scores against
-steganalyzers. Other steganalyzers than the ones used in this document will be
+be improved by replacing Canny with another edge filter.
+% We will try
+% to take into account the least significant bits too during all the
+% stages of the algorithm, hoping by doing so to be closer to the HUGO scores against
+% steganalyzers.
+Other steganalyzers than the ones used in this document will be
examined for the sake of completeness. Finally, the
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
+This section first presents the embedding scheme through its
+four main steps: the data encryption (Sect.~\ref{sub:bbs}),
+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 finally presented (Sect.\ref{sub:extract}) and a running example ends this section (Sect.~\ref{sub:xpl}).
+
+
The flowcharts given in Fig.~\ref{fig:sch}
summarize our steganography scheme denoted by
STABYLO, which stands for STeganography with cAnny, Bbs, binarY embedding at LOw cost.
-\subsection{Security Considerations}
+\subsection{Security Considerations}\label{sub:bbs}
Among methods of message encryption/decryption
(see~\cite{DBLP:journals/ejisec/FontaineG07} for a survey)
we implement the Blum-Goldwasser cryptosystem~\cite{Blum:1985:EPP:19478.19501}
this step computes a message $m$, which is the encrypted version of \textit{mess}.
-\subsection{Edge-Based Image Steganography}
+\subsection{Edge-Based Image Steganography}\label{sub:edge}
The edge-based image
on many kinds of architectures like FPGAs, smartphones, desktop machines, and
GPUs, we have chosen this edge detector for illustrative purpose.
+\JFC{il faudrait comparer les complexites des algo fuzy and canny}
+
+
This edge detection is applied on a filtered version of the image given
as input.
More precisely, only $b$ most
In our flowcharts, this is represented by ``edgeDetection(b bits)''.
Then only the 2 LSBs of pixels in the set of edges are returned if $b$ is 6,
and the LSB of pixels if $b$ is 7.
-Let $x$ be the sequence of these bits.
+
+
+
+
+
+Let $x$ be the sequence of these bits.
+The next section section presentsd how our scheme
+adapts when the size of $x$ is not sufficient for the message $m$ to embed.
+
+
-\JFC{il faudrait comparer les complexites des algo fuzy and canny}
+
+
+
+
+\subsection{Adaptive Embedding Rate}\label{sub:adaptive}
+Two strategies have been developed in our scheme,
+depending on the embedding rate that is either \emph{adaptive} or \emph{fixed}.
+In the former the embedding rate depends on the number of edge pixels.
+The higher it is, the larger the message length that can be inserted is.
+Practically, a set of edge pixels is computed according to the
+Canny algorithm with an high threshold.
+The message length is thus defined to be less than
+half of this set cardinality.
+If $x$ is then to short for $m$, the message is splitted into sufficient parts.
+In the latter, the embedding rate is defined as a percentage between the
+number of modified pixels and the length of the bit message.
+This is the classical approach adopted in steganography.
+Practically, the Canny algorithm generates
+a set of edge pixels related to a threshold that is decreasing
+until its cardinality
+is sufficient.
+
+
+
+Two methods may further be applied to select bits that
+will be modified.
+The first one randomly chooses the subset of pixels to modify by
+applying the BBS PRNG again. This method is further denoted as to \emph{sample}.
+The second one is a direct application of the
+STC algorithm~\cite{DBLP:journals/tifs/FillerJF11}.
+It is further referred to as \emph{adaptive+STC} and is detailled in the nex section.
+
+
+
% First of all, let us discuss about compexity of edge detetction methods.
-As argue in the introduction section, we do not adapt the parameters of the
-the edge detection as in~\cite{Luo:2010:EAI:1824719.1824720} but we modify
-the size of the embedding message. Practically, the lenght of $x$
-has to be at least twice as large
-as the size of the embedded encrypted message.
-Otherwise, a new image is used to hide the remaning part of the message.
-\subsection{Minimizing Distortion with Syndrome-Treillis Codes}
+\subsection{Minimizing Distortion with Syndrome-Treillis Codes}\label{sub:stc}
\input{stc}
-\subsection{Data Extraction}
+\subsection{Data Extraction}\label{sub:extract}
The message extraction summarized in Fig.~\ref{fig:sch:ext} follows data embedding
since there exists a reverse function for all its steps.
First of all, the same edge detection is applied (on the 7 first bits) to
Then the STC reverse algorithm is applied to retrieve the encrypted message.
Finally, the Blum-Goldwasser decryption function is executed and the original
message is extracted.
+
+
+\subsection{Running Example}\label{sub:xpl}
+In this example, the cover image is Lena
+which is a 512*512 image with 256 grayscale levels.
+The message is the poem Ulalume (E. A. Poe), which is constituted by 104 lines, 667
+words, and 3754 characters, \textit{i.e.} 30032 bits.
+Lena and the the first verses are given in Fig.~\ref{fig:lena}.
+
+\begin{figure}
+\begin{center}
+\begin{minipage}{0.4\linewidth}
+\includegraphics[width=3cm]{Lena.eps}
+\end{minipage}
+\begin{minipage}{0.59\linewidth}
+\begin{flushleft}
+\begin{scriptsize}
+The skies they were ashen and sober;\linebreak
+$~$ The leaves they were crisped and sere—\linebreak
+$~$ The leaves they were withering and sere;\linebreak
+It was night in the lonesome October\linebreak
+$~$ Of my most immemorial year;\linebreak
+It was hard by the dim lake of Auber,\linebreak
+$~$ In the misty mid region of Weir—\linebreak
+It was down by the dank tarn of Auber,\linebreak
+$~$ In the ghoul-haunted woodland of Weir.
+\end{scriptsize}
+\end{flushleft}
+\end{minipage}
+\end{center}
+\caption{Cover and message examples} \label{fig:lena}
+\end{figure}
+
+The edge detection returns 18641 and 18455 pixels when $b$ is
+respectively 7 and 6. These edges are represented in Fig.~\ref{fig:edge}
+
+
+\begin{figure}[t]
+ \begin{center}
+ \subfloat[$b$ is 7.]{
+ \begin{minipage}{0.49\linewidth}
+ \begin{center}
+ %\includegraphics[width=5cm]{emb.pdf}
+ \includegraphics[scale=0.15]{edge7.eps}
+ \end{center}
+ \end{minipage}
+ %\label{fig:sch:emb}
+ }%\hfill
+ \subfloat[$b$ is 6.]{
+ \begin{minipage}{0.49\linewidth}
+ \begin{center}
+ %\includegraphics[width=5cm]{rec.pdf}
+ \includegraphics[scale=0.15]{edge6.eps}
+ \end{center}
+ \end{minipage}
+ %\label{fig:sch:ext}
+ }%\hfill
+ \end{center}
+ \caption{Edge Detection wrt $b$.}
+ \label{fig:edge}
+\end{figure}
+
+
+
+In the former configuration, only 9320 bits are available
+for embeding whereas in the latter we have 9227.
+In the both case, about the third part of the poem is hidden into the cover.
+Results with \emph{adaptive+STC} strategy are presented in
+Fig.~\ref{fig:lenastego}.
+
+\begin{figure}[t]
+ \begin{center}
+ \subfloat[$b$ is 7.]{
+ \begin{minipage}{0.49\linewidth}
+ \begin{center}
+ %\includegraphics[width=5cm]{emb.pdf}
+ \includegraphics[scale=0.15]{lena7.eps}
+ \end{center}
+ \end{minipage}
+ %\label{fig:sch:emb}
+ }%\hfill
+ \subfloat[$b$ is 6.]{
+ \begin{minipage}{0.49\linewidth}
+ \begin{center}
+ %\includegraphics[width=5cm]{rec.pdf}
+ \includegraphics[scale=0.15]{lena6.eps}
+ \end{center}
+ \end{minipage}
+ %\label{fig:sch:ext}
+ }%\hfill
+ \end{center}
+ \caption{Stego Images wrt $b$.}
+ \label{fig:lenastego}
+\end{figure}
+
+
+Finally, differences between the original cover and the stego images
+are presented in Fig.~\ref{fig:lenadiff}. For each pixel pair of picel $X_{ij}$
+$Y_{ij}$, $X$ and $Y$ being the cover and the stego content respectively,
+The pixel value $V_{ij}$ of the difference is defined with the following map
+$$
+V_{ij}= \left\{
+\begin{array}{rcl}
+0 & \textrm{if} & X_{ij} = Y_{ij} \\
+75 & \textrm{if} & \abs{ (X_{ij} - Y_{ij})} = 1 \\
+75 & \textrm{if} & \abs{ (X_{ij} - Y_{ij})} = 2 \\
+225 & \textrm{if} & \abs{ (X_{ij} - Y_{ij})} = 1
+\end{array}
+\right.
+$$.
+This function allows to emphase differences between content.
+
+\begin{figure}[t]
+ \begin{center}
+ \subfloat[$b$ is 7.]{
+ \begin{minipage}{0.49\linewidth}
+ \begin{center}
+ %\includegraphics[width=5cm]{emb.pdf}
+ \includegraphics[scale=0.15]{diff7.eps}
+ \end{center}
+ \end{minipage}
+ %\label{fig:sch:emb}
+ }%\hfill
+ \subfloat[$b$ is 6.]{
+ \begin{minipage}{0.49\linewidth}
+ \begin{center}
+ %\includegraphics[width=5cm]{rec.pdf}
+ \includegraphics[scale=0.15]{diff6.eps}
+ \end{center}
+ \end{minipage}
+ %\label{fig:sch:ext}
+ }%\hfill
+ \end{center}
+ \caption{Differences with Lena's Cover wrt $b$.}
+ \label{fig:lenadiff}
+\end{figure}
