raph

diff --git a/experiments.tex b/experiments.tex
index 2fc8a68527d8156735290d45b82898090e7d4289..aa8a3e94dd8b4c872185249d20ffcedeb6fd4c44 100644 (file)
--- a/experiments.tex
+++ b/experiments.tex
@@ -50,7 +50,7 @@ If $b$ is 6, these values are respectively equal to
 \hline
 Schemes & \multicolumn{4}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR} \\
 \hline
 \hline
 Schemes & \multicolumn{4}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR} \\
 \hline

-Embedding &   Fixed & \multicolumn{3}{|c|}{Adaptive} &  \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
+Embedding &   Fixed & \multicolumn{3}{|c|}{Adaptive (about 6.35\%)} &  \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\

 \hline
 Rate &   10\% &  + sample &  +STC(7) & +STC(6) &  10\%&6.35\%& 10\%&6.35\%\\ 
 \hline
 \hline
 Rate &   10\% &  + sample &  +STC(7) & +STC(6) &  10\%&6.35\%& 10\%&6.35\%\\ 
 \hline


@@ -88,8 +88,8 @@ 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.
 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\%)


@@ -141,7 +141,7 @@ can be favorably executed thanks to an ensemble classifier.
 \hline
 Schemes & \multicolumn{4}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR}\\
 \hline
 \hline
 Schemes & \multicolumn{4}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR}\\
 \hline

-Embedding & Fixed &   \multicolumn{3}{|c|}{Adaptive}  & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
+Embedding & Fixed &   \multicolumn{3}{|c|}{Adaptive (about 6.35\%)}  & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\

 \hline
 Rate & 10\% &  + sample &   +STC(7) & +STC(6)   & 10\%& 6.35\%& 10\%& 6.35\%\\ 
 \hline
 \hline
 Rate & 10\% &  + sample &   +STC(7) & +STC(6)   & 10\%& 6.35\%& 10\%& 6.35\%\\ 
 \hline

diff --git a/main.tex b/main.tex
index c4bb7fc538bf378752d6948cd595c827c5faaaf0..002666aabed787782cfc450d34cbc13296c68851 100755 (executable)
--- a/main.tex
+++ b/main.tex
@@ -154,6 +154,7 @@ examined for the sake of completeness. Finally, the
 systematic replacement of all the LSBs of edges by binary digits provided
 by the BBS generator will be investigated, and the consequences of such a 
 replacement, in terms of security, will be discussed.
 systematic replacement of all the LSBs of edges by binary digits provided
 by the BBS generator will be investigated, and the consequences of such a 
 replacement, in terms of security, will be discussed.

+Furthermore, we plan to investigate information hiding on other models, high frequency for JPEG encoding for instance.

 
 
 \bibliographystyle{plain}
 
 
 \bibliographystyle{plain}

diff --git a/ourapproach.tex b/ourapproach.tex
index 9cf0384609ab84a68d9fe1c22ec114405e3bc807..9cce7a23196edf2328ab151c151f9dcce1a19e9c 100644 (file)
--- a/ourapproach.tex
+++ b/ourapproach.tex
@@ -3,7 +3,7 @@ four main steps: the data encryption (Sect.~\ref{sub:bbs}),
 the cover pixel selection (Sect.~\ref{sub:edge}),
 the adaptive payload considerations (Sect.~\ref{sub:adaptive}),
 and how the distortion has been minimized (Sect.~\ref{sub:stc}).
 the cover pixel selection (Sect.~\ref{sub:edge}),
 the adaptive payload considerations (Sect.~\ref{sub:adaptive}),
 and how the distortion has been minimized (Sect.~\ref{sub:stc}).

-The message extraction is finally presented  (Sect.\ref{sub:extract}) and a running example ends this section (Sect.~\ref{sub:xpl}). 
+The message extraction is finally presented  (Sect.~\ref{sub:extract}) and a running example ends this section (Sect.~\ref{sub:xpl}). 

 
 
 The flowcharts given in Fig.~\ref{fig:sch}
 
 
 The flowcharts given in Fig.~\ref{fig:sch}


@@ -99,7 +99,7 @@ As the Canny algorithm is well known and studied, fast, and implementable
 on many  kinds of architectures like FPGAs, smartphones,  desktop machines, and
 GPUs, we have chosen this edge detector for illustrative purpose.
 
 on many  kinds of architectures like FPGAs, smartphones,  desktop machines, and
 GPUs, we have chosen this edge detector for illustrative purpose.
 

-\JFC{il faudrait comparer les complexites des algo fuzy and canny}
+%\JFC{il faudrait comparer les complexites des algo fuzy and canny}

 
 
 This edge detection is applied on a filtered version of the image given 
 
 
 This edge detection is applied on a filtered version of the image given 


@@ -133,7 +133,7 @@ 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 the message length that can be inserted is.
 Practically, a set of edge pixels is computed according to the 
 In the former the embedding rate depends on the number of edge pixels.
 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.
+Canny algorithm with a high threshold.

 The message length is thus defined to be less than 
 half of this set cardinality.
 If $x$ is then too short for $m$, the message is split into sufficient parts
 The message length is thus defined to be less than 
 half of this set cardinality.
 If $x$ is then too short for $m$, the message is split into sufficient parts