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 is the message length that can be inserted.
+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 the half of this set cardinality.
+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 as \emph{adaptive+STC}.
+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.
-On our experiments and with the adaptive scheme,
-the average size of the message that can be embedded is 16445.
+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\%.
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
-a set of edge pixels with threshold that is decreasing until its cardinality
+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.
account the Human Visual System (HVS).
The other ones have been designed to tackle this problem.
-\begin{table}
+\begin{table*}
\begin{center}
-\begin{tabular}{|c|c|c||c|c|}
+\begin{tabular}{|c|c|c||c|c|c|}
\hline
-Schemes & \multicolumn{3}{|c|}{STABYLO} & HUGO\\
+Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}\\
\hline
-Embedding & \multicolumn{2}{|c||}{Adaptive} & Fixed & Fixed \\
+Embedding & \multicolumn{2}{|c||}{Adaptive} & Fixed & \multicolumn{2}{|c|}{Fixed} \\
\hline
-Rate & + STC & + sample & 10\% & 10\%\\
+Rate & + STC & + sample & 10\% & 10\%&6.35\%\\
\hline
-PSNR & 66.55 & 63.48 & 61.86 & 64.65 \\
+PSNR & 66.55 & 63.48 & 61.86 & 64.65 & 67.08 \\
\hline
-PSNR-HVS-M & 78.6 & 75.39 & 72.9 & 76.67\\
+PSNR-HVS-M & 78.6 & 75.39 & 72.9 & 76.67 & 79.23 \\
\hline
-BIQI & 28.3 & 28.28 & 28.4 & 28.28\\
+BIQI & 28.3 & 28.28 & 28.4 & 28.28 & 28.28 \\
\hline
-wPSNR & 86.43& 80.59 & 77.47& 83.03\\
+wPSNR & 86.43& 80.59 & 77.47& 83.03 & 87.8\\
\hline
\end{tabular}
\end{center}
\caption{Quality Measures of Steganography Approaches\label{table:quality}}
-\end{table}
+\end{table*}
Let us give an interpretation of these experiments.
First of all, the adaptive strategy produces images with lower distortion
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 better results with the strategy
-\emph{adaptive+STC} in a lightweight manner, as motivated in the introduction.
+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.
Let us now compare the STABYLO approach with other edge based steganography
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 the approaches~\cite{DBLP:journals/eswa/ChenCL10,Chang20101286}
+Next, both 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.
+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.
-\begin{table}
+\begin{table*}
\begin{center}
-\begin{tabular}{|c|c|c|c|c|}
+%\begin{small}
+\begin{tabular}{|c|c|c|c|c|c|}
\hline
-Schemes & \multicolumn{3}{|c|}{STABYLO} & HUGO\\
+Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}\\
\hline
-Embedding & \multicolumn{2}{|c|}{Adaptive} & Fixed & Fixed \\
+Embedding & \multicolumn{2}{|c|}{Adaptive} & Fixed & \multicolumn{2}{|c|}{Fixed} \\
\hline
-Rate & + STC & + sample & 10\% & 10\%\\
+Rate & + STC & + sample & 10\% & 10\%& 6.35\%\\
\hline
-AUMP & 0.39 & 0.33 & 0.22 & 0.50 \\
+AUMP & 0.39 & 0.33 & 0.22 & 0.50 & 0.50 \\
\hline
-Ensemble Classifier & 0.47 & 0.44 & 0.35 & 0.48 \\
+Ensemble Classifier & 0.47 & 0.44 & 0.35 & 0.48 & 0.49 \\
\hline
\end{tabular}
+%\end{small}
\end{center}
\caption{Steganalysing STABYLO\label{table:steganalyse}}
-\end{table}
+\end{table*}
Results show that our approach is more easily detectable than HUGO, which
is the most secure steganographic tool, as far as we know. However due to its
huge number of features integration, it is not lightweight, which justifies
-in authors' opinion the consideration of the proposed method.
+in the authors' opinion the consideration of the proposed method.
