-For the whole experiment, a set of 500 images is randomly extracted
+For whole experiments, a set of 500 images is randomly extracted
from the database taken from the BOSS contest~\cite{Boss10}.
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}.
+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 considered.
+The higher it is, the larger is the message length that can be inserted.
Practically, a set of edge pixels is computed according to the
-Canny algorithm with high threshold.
+Canny algorithm with an 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.
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.
+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.
Its corresponds to an average payload of 6.35\%.
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.
+bit message length, a STC step is again applied.
Otherwise, pixels are again randomly chosen with BBS.
\subsection{Image 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, four 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 BIQI~\cite{MB10,biqi11}, 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).
+account the Human Visual System (HVS).
The other last ones have been designed to tackle this problem.
\begin{table}
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\%)
+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.
+\emph{adaptive+STC} in a lightweight manner, as motivated in the introduction.
Let us now compare the STABYLO approach with other edge based steganography
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:
+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}
focus on increasing the payload while the PSNR is acceptable, but do not
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}.
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.
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 favorably executed thanks to an Ensemble Classifiers.
\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.
