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}) and 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 (Sect.~\ref{sub:xpl}).
The flowcharts given in Fig.~\ref{fig:sch}
summarize our steganography scheme denoted by
-STABYLO, which stands for STeganography with
+STABYLO, which stands for STe\-ga\-no\-gra\-phy with
Adaptive, Bbs, binarY embedding at LOw cost.
What follows are successively some details of the inner steps and the flows both inside
the embedding stage (Fig.~\ref{fig:sch:emb})
\begin{figure*}%[t]
\begin{center}
- \subfloat[Data Embedding.]{
- \begin{minipage}{0.49\textwidth}
+ \subfloat[Data Embedding]{
+ \begin{minipage}{0.4\textwidth}
\begin{center}
- %\includegraphics[width=5cm]{emb.pdf}
- \includegraphics[scale=0.45]{emb.ps}
+ %\includegraphics[scale=0.45]{emb}
+ \includegraphics[scale=0.4]{emb}
\end{center}
\end{minipage}
\label{fig:sch:emb}
}
-
- \subfloat[Data Extraction.]{
+\hfill
+ \subfloat[Data Extraction]{
\begin{minipage}{0.49\textwidth}
\begin{center}
- %\includegraphics[width=5cm]{rec.pdf}
- \includegraphics[scale=0.45]{rec.ps}
+ \includegraphics[scale=0.4]{dec}
\end{center}
\end{minipage}
\label{fig:sch:ext}
\subsection{Security considerations}\label{sub:bbs}
Among the 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}
+we implement the asymmetric
+Blum-Goldwasser cryptosystem~\cite{Blum:1985:EPP:19478.19501}
that is based on the Blum Blum Shub~\cite{DBLP:conf/crypto/ShubBB82}
pseudorandom number generator (PRNG) and the
XOR binary function.
-It has been indeed proven~\cite{DBLP:conf/crypto/ShubBB82} that this PRNG
+The main justification of this choice
+is that it has been proven~\cite{DBLP:conf/crypto/ShubBB82} that this PRNG
has the property of cryptographical security, \textit{i.e.},
for any sequence of $L$ output bits $x_i$, $x_{i+1}$, \ldots, $x_{i+L-1}$,
there is no algorithm, whose time complexity is polynomial in $L$, and
Many techniques have been proposed in the literature to detect
edges in images (whose noise has been initially reduced).
-They can be separated into two categories: first and second order detection
+They can be separated in two categories: first and second order detection
methods on the one hand, and fuzzy detectors on the other hand~\cite{KF11}.
-In first order methods like Sobel, Canny~\cite{Canny:1986:CAE:11274.11275}, \ldots,
+In first order methods like Sobel, Canny~\cite{Canny:1986:CAE:11274.11275}, and so on,
a first-order derivative (gradient magnitude, etc.) is computed
to search for local maxima, whereas in second order ones, zero crossings in a second-order derivative, like the Laplacian computed from the image,
are searched in order to find edges.
Canny filters, on their parts, are an old family of algorithms still remaining a state of the art edge detector. They can be well-approximated by first-order derivatives of Gaussians.
As the Canny algorithm is fast, well known, has been studied in depth, and is implementable
-on many kinds of architectures like FPGAs, smartphones, desktop machines, and
+on many kinds of architectures like FPGAs, smart phones, 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
-significant bits are concerned by this step, where
-the parameter $b$ is practically set with $6$ or $7$.
+More precisely, only $b$ most significant bits are concerned by this step,
+where the parameter $b$ is practically set with $6$ or $7$.
+Notice that 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.
If set with the same value $b$, the edge detection returns thus the same
set of pixels for both the cover and the stego image.
-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.
-
-
-
+Moreover, to provide edge gradient value,
+the Canny algorithm computes derivatives
+in the two directions with respect to a mask of size $T$.
+The higher $T$ is, the coarse the approach is. Practically,
+$T$ is set with $3$, $5$, or $7$.
+In our flowcharts, this step is represented by ``Edge Detection(b, T, X)''.
Let $x$ be the sequence of these bits.
-The next section presents how our scheme
-adapts when the size of $x$ is not sufficient for the message $m$ to embed.
+The next section presents how to adapt our scheme
+with respect to the size
+of the message $m$ to embed and the size of the cover $x$.
+
\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}.
+Two strategies have been developed in our approach,
+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 a high threshold.
+Canny algorithm with parameters $b=7$ and $T=3$.
The message length is thus defined to be less than
half of this set cardinality.
-If $x$ is then too short for $m$, the message is split into sufficient parts
+If $x$ is too short for $m$, the message is split into sufficient parts
and a new cover image should be used for the remaining part of the message.
-
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
+a set of edge pixels related to increasing values of $T$ and
until its cardinality
is sufficient. Even in this situation, our scheme is adapting
its algorithm to meet all the user's requirements.
applying the BBS PRNG again. This method is further denoted as a \emph{sample}.
Once this set is selected, a classical LSB replacement is applied to embed the
stego content.
-The second method is a direct application of the
-STC algorithm~\cite{DBLP:journals/tifs/FillerJF11}.
+The second method considers the last significant bits of all the pixels
+inside the previous map. It next directly applies the STC
+algorithm~\cite{DBLP:journals/tifs/FillerJF11}.
It is further referred to as \emph{STC} and is detailed in the next section.
-% First of all, let us discuss about compexity of edge detetction methods.
-% Let then $M$ and $N$ be the dimension of the original image.
-% According to~\cite{Hu:2007:HPE:1282866.1282944},
-% even if the fuzzy logic based edge detection methods~\cite{Tyan1993}
-% have promising results, its complexity is in $C_3 \times O(M \times N)$
-% whereas the complexity on the Canny method~\cite{Canny:1986:CAE:11274.11275}
-% is in $C_1 \times O(M \times N)$ where $C_1 < C_3$.
-% \JFC{Verifier ceci...}
-% In experiments detailled in this article, the Canny method has been retained
-% but the whole approach can be updated to consider
-% the fuzzy logic edge detector.
-
-
-
-
-\subsection{Minimizing distortion with syndrome-trellis codes}\label{sub:stc}
+\subsection{Minimizing distortion with Syndrome-Trellis Codes}\label{sub:stc}
\input{stc}
follows the data embedding approach
since there exists a reverse function for all its steps.
-More precisely, the same edge detection is applied on the $b$ first bits to
+More precisely, let $b$ be the most significant bits and
+$T$ be the size of the canny mask, both be given as a key.
+Thus, the same edge detection is applied on a stego content $Y$ to
produce the sequence $y$ of LSBs.
If the STC approach has been selected in embedding, the STC reverse
algorithm is directly executed to retrieve the encrypted message.
-This inverse function takes the $H$ matrix as a parameter.
+This inverse function takes the $\hat{H}$ matrix as a parameter.
Otherwise, \textit{i.e.}, if the \emph{sample} strategy is retained,
the same random bit selection than in the embedding step
is executed with the same seed, given as a key.
\begin{center}
\begin{minipage}{0.49\linewidth}
\begin{center}
-\includegraphics[scale=0.20]{Lena.eps}
+\includegraphics[scale=0.20]{lena512}
\end{center}
\end{minipage}
\begin{minipage}{0.49\linewidth}
\end{figure}
The edge detection returns 18,641 and 18,455 pixels when $b$ is
-respectively 7 and 6. These edges are represented in Figure~\ref{fig:edge}.
+respectively 7 and 6 and $T=3$.
+These edges are represented in Figure~\ref{fig:edge}.
When $b$ is 7, it remains one bit per pixel to build the cover vector.
-in this configuration, this leads to a cover vector of size 18,641 if b is 7
+This configuration leads to a cover vector of size 18,641 if b is 7
and 36,910 if $b$ is 6.
\begin{figure}[t]
\begin{minipage}{0.49\linewidth}
\begin{center}
%\includegraphics[width=5cm]{emb.pdf}
- \includegraphics[scale=0.20]{edge7.eps}
+ \includegraphics[scale=0.20]{edge7}
\end{center}
\end{minipage}
%\label{fig:sch:emb}
\begin{minipage}{0.49\linewidth}
\begin{center}
%\includegraphics[width=5cm]{rec.pdf}
- \includegraphics[scale=0.20]{edge6.eps}
+ \includegraphics[scale=0.20]{edge6}
\end{center}
\end{minipage}
%\label{fig:sch:ext}
}%\hfill
\end{center}
- \caption{Edge detection wrt $b$}
+ \caption{Edge detection wrt $b$ with $T=3$}
\label{fig:edge}
\end{figure}
The STC algorithm is optimized when the rate between message length and
-cover vector length is less than 1/2.
+cover vector length is lower than 1/2.
So, only 9,320 bits are available for embedding
in the configuration where $b$ is 7.
However, first experiments have shown that modifying this number of bits is too
easily detectable.
So, we choose to modify the same amount of bits (9,320) and keep STC optimizing
-which bits to change among the 36,910 bits.
+which bits to change among the 36,910 ones.
In the two cases, about the third part of the poem is hidden into the cover.
-Results with \emph{adaptive+STC} strategy are presented in
+Results with {Adaptive} and {STC} strategies are presented in
Fig.~\ref{fig:lenastego}.
\begin{figure}[t]
\begin{minipage}{0.49\linewidth}
\begin{center}
%\includegraphics[width=5cm]{emb.pdf}
- \includegraphics[scale=0.20]{lena7.eps}
+ \includegraphics[scale=0.20]{lena7}
\end{center}
\end{minipage}
%\label{fig:sch:emb}
\begin{minipage}{0.49\linewidth}
\begin{center}
%\includegraphics[width=5cm]{rec.pdf}
- \includegraphics[scale=0.20]{lena6.eps}
+ \includegraphics[scale=0.20]{lena6}
\end{center}
\end{minipage}
%\label{fig:sch:ext}
\right..
$$
This function allows to emphasize differences between contents.
+Notice that since $b$ is 7 in Fig.~\ref{fig:diff7}, the embedding is binary
+and this image only contains 0 and 75 values.
+Similarly, if $b$ is 6 as in Fig.~\ref{fig:diff6}, the embedding is ternary
+and the image contains all the values in $\{0,75,150,225\}$.
+
+
\begin{figure}[t]
\begin{center}
\begin{minipage}{0.49\linewidth}
\begin{center}
%\includegraphics[width=5cm]{emb.pdf}
- \includegraphics[scale=0.20]{diff7.eps}
+ \includegraphics[scale=0.20]{diff7}
\end{center}
\end{minipage}
- %\label{fig:sch:emb}
+ \label{fig:diff7}
}%\hfill
\subfloat[$b$ is 6.]{
\begin{minipage}{0.49\linewidth}
\begin{center}
%\includegraphics[width=5cm]{rec.pdf}
- \includegraphics[scale=0.20]{diff6.eps}
+ \includegraphics[scale=0.20]{diff6}
\end{center}
\end{minipage}
- %\label{fig:sch:ext}
+ \label{fig:diff6}
}%\hfill
\end{center}
\caption{Differences with Lena's cover wrt $b$}
