-For the whole experiment, a set of 500 images is randomly extracted
-from the database taken from the BOSS contest~\cite{Boss10}.
+For whole experiments, the whole set of 10,000 images
+of the BOSS contest~\cite{Boss10} database is taken.
In this set, each cover is a $512\times 512$
-grayscale digital image.
-
-
-\subsection{Adaptive Embedding Rate}
-
-Two strategies have been developed in our scheme with respect to the rate of
-embedding which 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 is the message length that can be considered.
-Practically, a set of edge pixels is computed according to the
-Canny algorithm with high threshold.
-The message length is thus defined to be the 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 as \emph{adaptive+STC}.
-The second one randomly choose 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 two.
-This constraint is indeed induced by the fact that the efficiency
-of the STC algorithm is unsatisfactory under that threshold.
-
-On our experiments and with the adaptive scheme,
-the average size of the message that can be embedded is 16445.
-Its corresponds to an average payload of 6.35\%.
+grayscale digital image in a RAW format.
+We restrict experiments to
+this set of cover images since this paper is more focused on
+the methodology than benchmarking.
+We use the matrices given in table~\ref{table:matrices:H}
+as introduced in~\cite{}, since these ones have experimentally
+be proven to have the best modification efficiency.
+
+\begin{table}
+$$
+\begin{array}{|l|l|}
+\textrm{rate} & \textrm{matrix generators} \\
+$\frac{1}{2} & \{71,109\}
+$\frac{1}{3} & \{95, 101, 121\}
+$\frac{1}{4} & \{81, 95, 107, 121\}
+$\frac{1}{5} & \{75, 95, 97, 105, 117\}
+$\frac{1}{6} & \{73, 83, 95, 103, 109, 123\}
+$\frac{1}{7} & \{69, 77, 93, 107, 111, 115, 121\}
+$\frac{1}{8} & \{69, 79, 81, 89, 93, 99, 107, 119\}
+$\frac{1}{9} & \{69, 79, 81, 89, 93, 99, 107, 119, 125]
+
+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 least detectable information hiding tool in spatial domain
+and the latter is the work that is the closest to ours, as far as we know.
+
+
+
+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.
+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 the modified pixels and the length of the bit message.
-This is the classical approach adopted in steganography.
-Practically, the Canny algorithm generates a
-a set of edge pixels with threshold that is decreasing until its cardinality
-is sufficient. If the set cardinality is more than twice larger than the
-bit message length an 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.
-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{PSECAL07,psnrhvsm11} ,
-the BIQI~\cite{MB10,biqi11} and
+the PSNR-HVS-M family~\cite{psnrhvsm11},
+%the BIQI~\cite{MB10},
+and
the weighted PSNR (wPSNR)~\cite{DBLP:conf/ih/PereiraVMMP01}.
The first one is widely used but does not take into
-account Human Visual System (HVS).
-The other last ones have been designed to tackle this problem.
+account the Human Visual System (HVS).
+The other ones have been designed to tackle this problem.
-\begin{table}
+If we apply them on the running example,
+the PSNR, PSNR-HVS-M, and wPSNR values are respectively equal to
+68.39, 79.85, and 89.71 for the stego Lena when $b$ is equal to 7.
+If $b$ is 6, these values are respectively equal to
+65.43, 77.2, and 89.35.
+
+
+
+
+\begin{table*}
\begin{center}
-\begin{tabular}{|c|c|c||c|c|}
+\begin{tabular}{|c|c|c||c|c|c|c|c|c|}
\hline
-Schemes & \multicolumn{3}{|c|}{STABYLO} & HUGO\\
+Schemes & \multicolumn{4}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR} \\
\hline
-Embedding & \multicolumn{2}{|c||}{Adaptive} & Fixed & Fixed \\
+Embedding & Fixed & \multicolumn{3}{|c|}{Adaptive (about 6.35\%)} & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
\hline
-Rate & + STC & + sample & 10\% & 10\%\\
+Rate & 10\% & + sample & +STC(7) & +STC(6) & 10\%&6.35\%& 10\%&6.35\%\\
\hline
-PSNR & 66.55 & 63.48 & 61.86 & 64.65 \\
+PSNR & 61.86 & 63.48 & 66.55 (\textbf{-0.8\%}) & 63.7 & 64.65 & {67.08} & 60.8 & 62.9\\
\hline
-PSNR-HVS-M & 78.6 & 75.39 & 72.9 & 76.67\\
+PSNR-HVS-M & 72.9 & 75.39 & 78.6 (\textbf{-0.8\%}) & 75.5 & 76.67 & {79.23} & 71.8 & 74.3\\
+%\hline
+%BIQI & 28.3 & 28.28 & 28.4 & 28.28 & 28.28 & 28.2 & 28.2\\
\hline
-BIQI & 28.3 & 28.28 & 28.4 & 28.28\\
-\hline
-wPSNR & 86.43& 80.59 & 77.47& 83.03\\
+wPSNR & 77.47 & 80.59 & 86.43(\textbf{-1.6\%})& 86.28 & 83.03 & {87.8} & 76.7 & 80.6\\
\hline
\end{tabular}
+
+\begin{footnotesize}
+\vspace{2em}
+Variances given in bold font express the quality differences between
+HUGO and STABYLO with STC+adaptive parameters.
+\end{footnotesize}
+
\end{center}
-\caption{Quality Measures of Steganography Approaches\label{table:quality}}
-\end{table}
+\caption{Quality measures of steganography approaches\label{table:quality}}
+\end{table*}
+
+
+Results are summarized in Table~\ref{table:quality}.
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.
+than the images resulting from the 10\% fixed strategy.
Numerical results are indeed always greater for the former strategy than
-for the latter, except for the BIQI metrics where differences are not relevant.
+for the latter one.
These results are not surprising since the adaptive strategy aims at
-embedding messages whose length is decided according to a higher threshold
+embedding messages whose length is decided according to an higher threshold
into the edge detection.
Let us focus on the quality of HUGO images: with a given fixed
-embedding rate (10\%)
+embedding rate (10\%),
HUGO always produces images whose quality is higher than the STABYLO's one.
-However, our approach nevertheless provides better results with the strategy
-adaptive+STC in a lightweight manner, as motivated in the introduction.
+However our approach is always better than EAISLSBMR since this one may modify
+the two least significant bits.
+
+If we combine \emph{adaptive} and \emph{STC} strategies
+(which leads to an average embedding rate equal to 6.35\%)
+our approach provides metrics equivalent to those provided by HUGO.
+In this column STC(7) stands for embedding data in the LSB whereas
+in STC(6), data are hidden in the two last significant bits.
+
+
+
+The quality variance between HUGO and STABYLO for these parameters
+is given in bold font. It is always close to 1\% which confirms
+the objective presented in the motivations:
+providing an efficient steganography approach with a lightweight manner.
Let us now compare the STABYLO approach with other edge based steganography
-schemes with respect to the image quality.
-First of all, the Edge Adaptive
-scheme detailed in~\cite{Luo:2010:EAI:1824719.1824720}
-executed with a 10\% embedding rate
-has the same PSNR but a lower wPSNR than our:
-these two metrics are respectively equal to 61.9 and 68.9.
-Next both the approaches~\cite{DBLP:journals/eswa/ChenCL10,Chang20101286}
-focus on increasing the payload while the PSNR is acceptable, but do not
-give quality metrics for fixed embedding rate from a large base of images.
-Our approach outperforms the former thanks to the introduction of the STC
-algorithm.
+approaches, namely~\cite{DBLP:journals/eswa/ChenCL10,Chang20101286}.
+These two schemes focus on increasing the
+payload while the PSNR is acceptable, but do not
+give quality metrics for fixed embedding rates from a large base of images.
-\subsection{Steganalysis}
+\subsection{Steganalysis}\label{sub:steg}
-The quality of our approach has been evaluated through the two
+The steganalysis quality of our approach has been evaluated through the two
AUMP~\cite{Fillatre:2012:ASL:2333143.2333587}
and Ensemble Classifier~\cite{DBLP:journals/tifs/KodovskyFH12} based steganalysers.
-Both aims at detecting hidden bits in grayscale natural images and are
-considered as the state of the art of steganalysers in spatial domain~\cite{FK12}.
+Both aim at detecting hidden bits in grayscale natural images and are
+considered as state of the art steganalysers in the spatial domain~\cite{FK12}.
The former approach is based on a simplified parametric model of natural images.
-Parameters are firstly estimated and a adaptive Asymptotically Uniformly Most Powerful
-(AUMP) test is designed (theoretically and practically) to check whether
-a natural image has stego content or not.
+Parameters are firstly estimated and an adaptive Asymptotically Uniformly Most Powerful
+(AUMP) test is designed (theoretically and practically), to check whether
+an image has stego content or not.
+This approach is dedicated to verify whether LSB has been modified or not.
In the latter, the authors show that the
-machine learning step, (which is often
-implemented as support vector machine)
-can be a favourably executed thanks to an Ensemble Classifiers.
+machine learning step, which is often
+implemented as a support vector machine,
+can be favorably executed thanks to an ensemble classifier.
-
-\begin{table}
+\begin{table*}
\begin{center}
-\begin{tabular}{|c|c|c|c|c|}
+%\begin{small}
+\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
\hline
-Schemes & \multicolumn{3}{|c|}{STABYLO} & HUGO\\
+Schemes & \multicolumn{4}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR}\\
\hline
-Embedding & \multicolumn{2}{|c|}{Adaptive} & Fixed & Fixed \\
+Embedding & Fixed & \multicolumn{3}{|c|}{Adaptive (about 6.35\%)} & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
\hline
-Rate & + STC & + sample & 10\% & 10\%\\
+Rate & 10\% & + sample & +STC(7) & +STC(6) & 10\%& 6.35\%& 10\%& 6.35\%\\
\hline
-AUMP & 0.39 & 0.33 & 0.22 & 0.50 \\
+AUMP & 0.22 & 0.33 & 0.39 & 0.45 & 0.50 & 0.50 & 0.49 & 0.50 \\
\hline
-Ensemble Classifier & 0.47 & 0.44 & 0.35 & 0.48 \\
+Ensemble Classifier & 0.35 & 0.44 & 0.47 & 0.47 & 0.48 & 0.49 & 0.43 & 0.46 \\
\hline
\end{tabular}
+%\end{small}
\end{center}
\caption{Steganalysing STABYLO\label{table:steganalyse}}
-\end{table}
+\end{table*}
+
+Results are summarized in Table~\ref{table:steganalyse}.
+First of all, STC outperforms the sample strategy for the two steganalysers, as
+already noticed in the quality analysis presented in the previous section.
+Next, our approach is more easily detectable than HUGO, which
+is the most secure steganographic tool, as far as we know.
+However by combining \emph{adaptive} and \emph{STC} strategies
+our approach obtains similar results to HUGO ones.
-Results show that our approach is more easily detectable than HUGO which is
-is the more secure steganography tool, as far we know. However due to its
-huge number of features integration, it is not lightweight.
+However due to its
+huge number of integration features, it is not lightweight, which justifies
+in the authors' opinion the consideration of the proposed method.
