apres relecture Christophe

diff --git a/intro.tex b/intro.tex
index b17515cf3e255df5c77b7d84c9e4466a3bd058c4..3ccf316baec8a49eb2a65bb71a1058d26b04b69f 100644 (file)
--- a/intro.tex
+++ b/intro.tex
@@ -1,7 +1,8 @@
 
 This research work takes place into the field of information hiding, considerably developed
 these last two decades. The proposed method for 
 
 This research work takes place into the field of information hiding, considerably developed
 these last two decades. The proposed method for 

-steganography considers digital images as covers, it belongs in the well investigated large category
+steganography considers digital images as covers.
+It belongs in the well investigated large category

 of spatial least significant bits (LSBs) replacement schemes.
 Let us recall that, in this LSBR category, a subset of all the LSBs of the cover image is modified 
 with a secret bit stream depending on: a secret key, the cover, and the message to embed.
 of spatial least significant bits (LSBs) replacement schemes.
 Let us recall that, in this LSBR category, a subset of all the LSBs of the cover image is modified 
 with a secret bit stream depending on: a secret key, the cover, and the message to embed.


@@ -45,7 +46,9 @@ modification minimizes a distortion function.
 This distortion may be computed thanks to feature vectors that are embedded for instance in steganalysers
 referenced above. 
 Highly Undetectable steGO (HUGO) method~\cite{DBLP:conf/ih/PevnyFB10} is one of the most efficient instance of such a scheme.
 This distortion may be computed thanks to feature vectors that are embedded for instance in steganalysers
 referenced above. 
 Highly Undetectable steGO (HUGO) method~\cite{DBLP:conf/ih/PevnyFB10} is one of the most efficient instance of such a scheme.

-It takes into account so-called SPAM features (whose size is larger than $10^7$) to avoid overfitting a particular 
+It takes into account so-called SPAM features 
+%(whose size is larger than $10^7$) 
+to avoid overfitting a particular 

 steganalyser. Thus a distortion measure for each pixel is individually determined as the sum of differences between
 features of SPAM computed from the cover and from the stego images.
 Thanks to this features set, HUGO allows to embed $7\times$ longer messages with the same level of 
 steganalyser. Thus a distortion measure for each pixel is individually determined as the sum of differences between
 features of SPAM computed from the cover and from the stego images.
 Thanks to this features set, HUGO allows to embed $7\times$ longer messages with the same level of 

diff --git a/main.tex b/main.tex
index fcf92c3b4aa40269ed0c7e121da97352fcb70965..0d0e95b6ed7b9ce8d6939b7e9bbebd1c4c1d724e 100755 (executable)
--- a/main.tex
+++ b/main.tex
@@ -1,4 +1,4 @@
-\documentclass[journal]{IEEEtran}
+\documentclass{acm_proc_article-sp}

 \usepackage{subfig}
 \usepackage{color}
 \usepackage{graphicx}
 \usepackage{subfig}
 \usepackage{color}
 \usepackage{graphicx}


@@ -23,18 +23,19 @@ edge-based steganographic approach}
   \{jean-francois.couchot, raphael.couturier, christophe.guyeux\}@femto-st.fr\\
  $*:$ Authors in alphabetic order.\\
 }
   \{jean-francois.couchot, raphael.couturier, christophe.guyeux\}@femto-st.fr\\
  $*:$ Authors in alphabetic order.\\
 }

-\newcommand{\JFC}[1]{\begin{color}{green}\textit{#1}\end{color}}
-\newcommand{\RC}[1]{\begin{color}{red}\textit{#1}\end{color}}
-\newcommand{\CG}[1]{\begin{color}{blue}\textit{#1}\end{color}}
+\newcommand{\JFC}[1]{\begin{color}{green}\textit{}\end{color}}
+\newcommand{\RC}[1]{\begin{color}{red}\textit{}\end{color}}
+\newcommand{\CG}[1]{\begin{color}{blue}\textit{}\end{color}}

 % make the title area
 \maketitle
 
 
 
 % make the title area
 \maketitle
 
 
 

-\begin{IEEEkeywords}
+

 %IEEEtran, journal, \LaTeX, paper, template.
 %IEEEtran, journal, \LaTeX, paper, template.

-Steganography, least-significant-bit (LSB)-based steganography, edge detection, Canny filter, security, syndrome treillis code.
-\end{IEEEkeywords}
+\keywords{Steganography, least-significant-bit (LSB)-based steganography, edge detection, Canny filter, security, syndrome treillis code}
+
+\maketitle

 
 \begin{abstract}
 A novel steganographic method called STABYLO is introduced in this research work.
 
 \begin{abstract}
 A novel steganographic method called STABYLO is introduced in this research work.


@@ -51,7 +52,7 @@ a scheme that can reasonably face up-to-date steganalysers.
 
 
 
 
 
 

-\IEEEpeerreviewmaketitle
+

 
 
 
 
 
 


@@ -69,7 +70,7 @@ a scheme that can reasonably face up-to-date steganalysers.
 \section{Conclusion}\label{sec:concl}
 
 The STABYLO algorithm, whose acronym means STeganography 
 \section{Conclusion}\label{sec:concl}
 
 The STABYLO algorithm, whose acronym means STeganography 

-with Canny, Bbs, binarY embedding at LOw cost, has been introduced 
+with cAnny, Bbs, binarY embedding at LOw cost, has been introduced 

 in this document as an efficient method having comparable, though
 somewhat smaller, security than the well known
 Highly Undetectable steGO (HUGO) steganographic scheme.
 in this document as an efficient method having comparable, though
 somewhat smaller, security than the well known
 Highly Undetectable steGO (HUGO) steganographic scheme.

diff --git a/ourapproach.tex b/ourapproach.tex
index 02d8735c39851a9319072d828b81292f5adc3cc9..3319b6a26a317c008a28c1df87d7e666fece6d1a 100644 (file)
--- a/ourapproach.tex
+++ b/ourapproach.tex
@@ -1,5 +1,5 @@
 The flowcharts given in Fig.~\ref{fig:sch} summarize our steganography scheme denoted by
 The flowcharts given in Fig.~\ref{fig:sch} summarize our steganography scheme denoted by

-STABYLO, which stands for STeganography with Canny, Bbs, binarY embedding at LOw cost.
+STABYLO, which stands for STeganography with cAnny, Bbs, binarY embedding at LOw cost.

 What follows successively details all the inner steps and flows inside 
 both the embedding stage (Fig.~\ref{fig:sch:emb}) 
 and the extraction one (Fig.~\ref{fig:sch:ext}).
 What follows successively details all the inner steps and flows inside 
 both the embedding stage (Fig.~\ref{fig:sch:emb}) 
 and the extraction one (Fig.~\ref{fig:sch:ext}).


@@ -169,7 +169,8 @@ polynomial time.
 \subsection{Data Extraction}
 Message extraction summarized in Fig.~\ref{fig:sch:ext} follows data embedding
 since there exists a reverse function for all its steps.
 \subsection{Data Extraction}
 Message extraction summarized in Fig.~\ref{fig:sch:ext} follows data embedding
 since there exists a reverse function for all its steps.

-First of all, the same edge detection is applied (on the 7 first bits) to get set,
+First of all, the same edge detection is applied (on the 7 first bits) to 
+get the set of LSBs,

 which is  sufficiently large with respect to the message size given as a key.  
 Then the STC reverse algorithm is applied to retrieve the encrypted message.
 Finally, the Blum-Goldwasser decryption function is executed and the original
 which is  sufficiently large with respect to the message size given as a key.  
 Then the STC reverse algorithm is applied to retrieve the encrypted message.
 Finally, the Blum-Goldwasser decryption function is executed and the original

diff --git a/stc.tex b/stc.tex
index a5442786bbf0d41848c477d8bae9d75756763ea4..f87104b0bc8f3c3f17d744ba4bb3f9ad74a984c5 100644 (file)
--- a/stc.tex
+++ b/stc.tex
@@ -1,3 +1,5 @@
+To make this article self-contained, this section recalls
+basis of the Syndrome Treillis Codes  (STC).

 Let 
 $x=(x_1,\ldots,x_n)$ be the $n$-bits cover vector of the image $X$, 
 $m$ be the message to embed, and 
 Let 
 $x=(x_1,\ldots,x_n)$ be the $n$-bits cover vector of the image $X$, 
 $m$ be the message to embed, and 


@@ -60,7 +62,7 @@ $2^n-1$ pixels needs $1-1/2^n$ average changes.
 
 Unfortunately, for any given $H$, finding $y$ that solves $Hy=m$ and  
 that minimizes $D_X(x,y)$, has an exponential complexity with respect to $n$. 
 
 Unfortunately, for any given $H$, finding $y$ that solves $Hy=m$ and  
 that minimizes $D_X(x,y)$, has an exponential complexity with respect to $n$. 

-The Syndrome-Trellis Codes  (STC) 
+The Syndrome-Trellis Codes  

 presented by Filler \emph{et al.} in~\cite{DBLP:conf/mediaforensics/FillerJF10} 
 is a practical solution to this complexity. Thanks to this contribution,
 the solving algorithm has a linear complexity with respect to $n$.
 presented by Filler \emph{et al.} in~\cite{DBLP:conf/mediaforensics/FillerJF10} 
 is a practical solution to this complexity. Thanks to this contribution,
 the solving algorithm has a linear complexity with respect to $n$.