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}.
-The second one randomly choose the subset of pixels to modify by
+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.
In the latter, the embedding rate is defined as a percentage between the
-number of the modified pixels and the length of the bit message.
+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
The visual quality of the STABYLO scheme is evaluated in this section.
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 PSNR-HVS-M family~\cite{PSECAL07,psnrhvsm11},
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 the Human Visual System (HVS).
-The other last ones have been designed to tackle this problem.
+The other ones have been designed to tackle this problem.
\begin{table}
\begin{center}
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 relevant.
+for the latter, except for the BIQI metrics where differences are not really relevant.
These results are not surprising since the adaptive strategy aims at
-embedding messages whose length is decided according to a higher threshold
+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\%),
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 the 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.
Our approach outperforms the former thanks to the introduction of the STC
(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 favorably executed thanks to an Ensemble Classifiers.
+machine learning step, which is often
+implemented as support vector machine,
+can be favorably executed thanks to an ensemble classifier.
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.
+huge number of features integration, it is not lightweight, which justifies
+in authors' opinion the consideration of the proposed method.
