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 refered as \emph{adaptive+STC}.
+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
\begin{center}
\begin{tabular}{|c|c|c||c|c|}
\hline
- & \multicolumn{2}{|c||}{Adaptive} & fixed & HUGO \\
-Embedding rate & + STC & + sample & 10\% & 10\%\\
+Schemes & \multicolumn{3}{|c|}{STABYLO} & HUGO\\
+\hline
+Embedding & \multicolumn{2}{|c||}{Adaptive} & Fixed & Fixed \\
+\hline
+Rate & + STC & + sample & 10\% & 10\%\\
\hline
PSNR & 66.55 & 63.48 & 61.86 & 64.65 \\
\hline
\hline
\end{tabular}
\end{center}
-\caption{Quality measures of our steganography approach\label{table:quality}}
+\caption{Quality Measures of Steganography Approaches\label{table:quality}}
\end{table}
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.
Numerical results are indeed always greater for the former strategy than
-for the latter, except for the BIQI metrics where differences are not relevent.
+for the latter, except for the BIQI metrics where differences are not relevant.
These results are not surprising since the adaptive strategy aims at
embedding messages whose length is decided according to a 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 beter results with the strategy
+However, our approach nevertheless provides better results with the strategy
adaptive+STC in a lightweight manner, as motivated in the introduction.
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.
+can be a favorably executed thanks to an Ensemble Classifiers.
\hline
Schemes & \multicolumn{3}{|c|}{STABYLO} & HUGO\\
\hline
-Embedding rate & \multicolumn{2}{|c|}{Adaptive} & 10 \% & 10 \%\\
- & + STC & + sample & & \\
-
+Embedding & \multicolumn{2}{|c|}{Adaptive} & Fixed & Fixed \\
+\hline
+Rate & + STC & + sample & 10\% & 10\%\\
\hline
-AUMP & 0.39 & & 0.22 & 0.50 \\
+AUMP & 0.39 & 0.33 & 0.22 & 0.50 \\
\hline
-Ensemble Classifier & 0.47 & & 0.35 & 0.48 \\
+Ensemble Classifier & 0.47 & 0.44 & 0.35 & 0.48 \\
\hline
\end{tabular}
\end{table}
-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
+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.
