of the BOSS contest~\cite{Boss10} database is taken.
In this set, each cover is a $512\times 512$
grayscale digital image in a RAW format.
of the BOSS contest~\cite{Boss10} database is taken.
In this set, each cover is a $512\times 512$
grayscale digital image in a RAW format.
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}.
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}.
-The former is the less detectable information hiding tool in spatial domain
-and the later is the work which is close to ours, as far as we know.
+The former is the least detectable information hiding tool in spatial domain
+and the latter is the work that is the closest to ours, as far as we know.
the average size of the message that can be embedded is 16,445 bits.
Its corresponds to an average payload of 6.35\%.
The two other tools will then be compared with this payload.
the average size of the message that can be embedded is 16,445 bits.
Its corresponds to an average payload of 6.35\%.
The two other tools will then be compared with this payload.
The visual quality of the STABYLO scheme is evaluated in this section.
For the sake of completeness, three metrics are computed in these experiments:
the Peak Signal to Noise Ratio (PSNR),
The visual quality of the STABYLO scheme is evaluated in this section.
For the sake of completeness, three metrics are computed in these experiments:
the Peak Signal to Noise Ratio (PSNR),
Let us give an interpretation of these experiments.
First of all, the adaptive strategy produces images with lower distortion
Let us give an interpretation of these experiments.
First of all, the adaptive strategy produces images with lower distortion
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.
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 always outperforms EAISLSBMR since this one may modify
-the two least significant bits whereas STABYLO only alter LSB.
+However our approach is always better than EAISLSBMR since this one may modify
+the two least significant bits.
If we combine \emph{adaptive} and \emph{STC} strategies
(which leads to an average embedding rate equal to 6.35\%)
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.
+our approach provides metrics equivalent to those provided by HUGO.
+In this column STC(7) stands for embedding data in the LSB whereas
+in STC(6), data are hidden in the two last significant bits.
+
+
+
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:
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:
The steganalysis quality of our approach has been evaluated through the two
AUMP~\cite{Fillatre:2012:ASL:2333143.2333587}
and Ensemble Classifier~\cite{DBLP:journals/tifs/KodovskyFH12} based steganalysers.
The steganalysis quality of our approach has been evaluated through the two
AUMP~\cite{Fillatre:2012:ASL:2333143.2333587}
and Ensemble Classifier~\cite{DBLP:journals/tifs/KodovskyFH12} based steganalysers.
-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}.
+Both aim at detecting hidden bits in grayscale natural images and are
+considered as state of the art steganalysers in the spatial domain~\cite{FK12}.
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
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
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
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
Next, our approach is more easily detectable than HUGO, which
is the most secure steganographic tool, as far as we know.
However by combining \emph{adaptive} and \emph{STC} strategies
Next, our approach is more easily detectable than HUGO, which
is the most secure steganographic tool, as far as we know.
However by combining \emph{adaptive} and \emph{STC} strategies
