-For whole experiments, the whole set of 10000 images
+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 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 less detectable information hiding tool in spatial domain
-and the later is the work which is close to ours, as far as we know.
+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.
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
+Sections~\ref{sub:quality} and~\ref{sub:steg} respectively present
the quality analysis and the security of our scheme.
-\subsection{Image Quality}\label{sub:quality}
+\subsection{Image quality}\label{sub:quality}
The visual quality of the STABYLO scheme is evaluated in this section.
For the sake of completeness, three metrics are computed in these experiments:
the Peak Signal to Noise Ratio (PSNR),
\begin{table*}
\begin{center}
-\begin{tabular}{|c|c|c||c|c|c|c|c|}
+\begin{tabular}{|c|c|c||c|c|c|c|c|c|}
\hline
-Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR} \\
+Schemes & \multicolumn{4}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR} \\
\hline
-Embedding & Fixed & \multicolumn{2}{|c|}{Adaptive} & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
+Embedding & Fixed & \multicolumn{3}{|c|}{Adaptive (about 6.35\%)} & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
\hline
-Rate & 10\% & + sample & + STC & 10\%&6.35\%& 10\%&6.35\%\\
+Rate & 10\% & + sample & +STC(7) & +STC(6) & 10\%&6.35\%& 10\%&6.35\%\\
\hline
-PSNR & 61.86 & 63.48 & 66.55 (\textbf{-0.8\%}) & 64.65 & {67.08} & 60.8 & 62.9\\
+PSNR & 61.86 & 63.48 & 66.55 (\textbf{-0.8\%}) & 63.7 & 64.65 & {67.08} & 60.8 & 62.9\\
\hline
-PSNR-HVS-M & 72.9 & 75.39 & 78.6 (\textbf{-0.8\%}) & 76.67 & {79.23} & 61.3 & 63.4\\
+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
-wPSNR & 77.47 & 80.59 & 86.43(\textbf{-1.6\%}) & 83.03 & {87.8} & 76.7 & 80.6\\
+wPSNR & 77.47 & 80.59 & 86.43(\textbf{-1.6\%})& 86.28 & 83.03 & {87.8} & 76.7 & 80.6\\
\hline
\end{tabular}
\end{footnotesize}
\end{center}
-\caption{Quality Measures of Steganography Approaches\label{table:quality}}
+\caption{Quality measures of steganography approaches\label{table:quality}}
\end{table*}
-Results are summarized into the Table~\ref{table:quality}.
+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.
+for the latter one.
These results are not surprising since the adaptive strategy aims at
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\%),
HUGO always produces images whose quality is higher than the STABYLO's one.
-However our approach always outperforms EAISLSBMR since this one may modify
-the two least significant bits whereas STABYLO only alter LSB.
+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 equivalent metrics than HUGO.
+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:
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 an adaptive Asymptotically Uniformly Most Powerful
(AUMP) test is designed (theoretically and practically), to check whether
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,
+implemented as a support vector machine,
can be favorably executed thanks to an ensemble classifier.
\begin{table*}
\begin{center}
%\begin{small}
-\begin{tabular}{|c|c|c|c|c|c|c|c|}
+\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
\hline
-Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR}\\
+Schemes & \multicolumn{4}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR}\\
\hline
-Embedding & Fixed & \multicolumn{2}{|c|}{Adaptive} & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
+Embedding & Fixed & \multicolumn{3}{|c|}{Adaptive (about 6.35\%)} & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
\hline
-Rate & 10\% & + sample & + STC & 10\%& 6.35\%& 10\%& 6.35\%\\
+Rate & 10\% & + sample & +STC(7) & +STC(6) & 10\%& 6.35\%& 10\%& 6.35\%\\
\hline
-AUMP & 0.22 & 0.33 & 0.39 & 0.50 & 0.50 & 0.49 & 0.50 \\
+AUMP & 0.22 & 0.33 & 0.39 & 0.45 & 0.50 & 0.50 & 0.49 & 0.50 \\
\hline
-Ensemble Classifier & 0.35 & 0.44 & 0.47 & 0.48 & 0.49 & 0.43 & 0.46 \\
+Ensemble Classifier & 0.35 & 0.44 & 0.47 & 0.47 & 0.48 & 0.49 & 0.43 & 0.46 \\
\hline
\end{tabular}
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 than HUGO ones.
+our approach obtains similar results to HUGO ones.
+
However due to its
-huge number of features integration, it is not lightweight, which justifies
+huge number of integration features, it is not lightweight, which justifies
in the authors' opinion the consideration of the proposed method.
