We restrict experiments to
this set of cover images since this paper is more focussed on
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}.
+
+
-\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}.
+\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.
For the sake of completeness, four metrics are computed in these experiments:
the Peak Signal to Noise Ratio (PSNR),
the PSNR-HVS-M family~\cite{psnrhvsm11},
-the BIQI~\cite{MB10}, and
+%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 the Human Visual System (HVS).
The other ones have been designed to tackle this problem.
+
+
+
\begin{table*}
\begin{center}
-\begin{tabular}{|c|c|c||c|c|c|}
-\hline
-Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}\\
+\begin{tabular}{|c|c|c||c|c|c|c|c|}
\hline
-Embedding & \multicolumn{2}{|c||}{Adaptive} & Fixed & \multicolumn{2}{|c|}{Fixed} \\
+Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR} \\
\hline
-Rate & + STC & + sample & 10\% & 10\%&6.35\%\\
+Embedding & \multicolumn{2}{|c||}{Adaptive} & Fixed & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
\hline
-PSNR & 66.55 & 63.48 & 61.86 & 64.65 & 67.08 \\
+Rate & + STC & + sample & 10\% & 10\%&6.35\%& 10\%&6.35\%\\
\hline
-PSNR-HVS-M & 78.6 & 75.39 & 72.9 & 76.67 & 79.23 \\
+PSNR & 66.55 (\textbf{-0.8\%}) & 63.48 & 61.86 & 64.65 & {67.08} & 60.8 & 62.9\\
\hline
-BIQI & 28.3 & 28.28 & 28.4 & 28.28 & 28.28 \\
+PSNR-HVS-M & 78.6 (\textbf{-0.8\%}) & 75.39 & 72.9 & 76.67 & {79.23} & 61.3 & 63.4\\
+%\hline
+%BIQI & 28.3 & 28.28 & 28.4 & 28.28 & 28.28 & 28.2 & 28.2\\
\hline
-wPSNR & 86.43& 80.59 & 77.47& 83.03 & 87.8\\
+wPSNR & 86.43(\textbf{-1.6\%}) & 80.59 & 77.47& 83.03 & {87.8} & & 80.6\\
\hline
\end{tabular}
\end{center}
-\caption{Quality Measures of Steganography Approaches\label{table:quality}}
+\caption{Quality Measures of Steganography Approaches\label{table:quality}}
+\label{table:quality}
\end{table*}
-Let us give an interpretation of these experiments.
+
+
+Results are summarized into the Table~\ref{table:quality}.
+Let us give an interpretation of these first 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
-for the latter, except for the BIQI metrics where differences are not really relevant.
+for the latter.
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 nevertheless provides equivalent
-results with the strategy
-\emph{adaptive+STC} than HUGO with an average embedding rate set to
-6.35\%.
-This occurs with a lightweight manner, as motivated in the introduction.
+However our appraoch always outperforms EAISLSBMR since this one may modify
+the two least significant bits whereas STABYLO only alter LSB.
+
+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.
+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 ours:
-these two metrics are respectively equal to 61.9 and 68.9.
-Next, both approaches~\cite{DBLP:journals/eswa/ChenCL10,Chang20101286}
-focus on increasing the payload while the PSNR is acceptable, but do not
+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.
-Our approach outperforms the former thanks to the introduction of the STC
-algorithm.
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
-an image has stego content or not.
+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 favorably executed thanks to an ensemble classifier.
-
+%citer le second tableau, comparer avec EAISLSBMR
\begin{table*}
\begin{center}
%\begin{small}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
\hline
-Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAIS}\\
+Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR}\\
\hline
Embedding & \multicolumn{2}{|c|}{Adaptive} & Fixed & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
\hline
Rate & + STC & + sample & 10\% & 10\%& 6.35\%& 10\%& 6.35\%\\
\hline
-AUMP & 0.39 & 0.33 & 0.22 & 0.50 & 0.50 & & \\
+AUMP & 0.39 & 0.33 & 0.22 & 0.50 & 0.50 & 0.49 & 0.50 \\
\hline
-Ensemble Classifier & \textbf{0.47} & 0.44 & 0.35 & 0.48 & 0.49 & 0.22 & 0.46 \\
+Ensemble Classifier & \textbf{0.47} & 0.44 & 0.35 & 0.48 & 0.49 & 0.43 & 0.46 \\
\hline
\end{tabular}
