mineur

diff --git a/analysis.tex b/analysis.tex
index 4ae4244fa96ed20806e035d24270587d63fab798..2af26fbe6674bf124e14332de95af957664eaad8 100644 (file)
--- a/analysis.tex
+++ b/analysis.tex
@@ -33,12 +33,3 @@ scheme can avoid the LSB replacement style asymmetry, and
 thus it should make the detection slightly more difficult than the
 LSBM approach based on our experiments
 
 thus it should make the detection slightly more difficult than the
 LSBM approach based on our experiments
 

-random LSB selection: coarse, easily tractable, easilly detactable
-
-
-
-feature-based : precise but time consuming 
-
-security need : message has to be encrypted before beeing introduced. Using efficient and secured cryptographic approach
-
-LSBR: to coarse (apply hugo breaker on it)

diff --git a/biblio.bib b/biblio.bib
index 47c0c0038d288badd2b9a353e93c5cc76d66cb4c..2ddab01ce654eb78e41cc63d0db3f55e281694f5 100644 (file)
--- a/biblio.bib
+++ b/biblio.bib
@@ -374,3 +374,104 @@ author    = {Jessica J. Fridrich and
   OPTannote =   {}
 }
 
   OPTannote =   {}
 }
 

+
+
+
+
+@article{Liu:2008:FMP:1284917.1285196,
+ author = {Liu, Qingzhong and Sung, Andrew H. and Chen, Zhongxue and Xu, Jianyun},
+ title = {Feature mining and pattern classification for steganalysis of LSB matching steganography in grayscale images},
+ journal = {Pattern Recogn.},
+ issue_date = {January, 2008},
+ volume = {41},
+ number = {1},
+ month = jan,
+ year = {2008},
+ issn = {0031-3203},
+ pages = {56--66},
+ numpages = {11},
+ url = {http://dx.doi.org/10.1016/j.patcog.2007.06.005},
+ doi = {10.1016/j.patcog.2007.06.005},
+ acmid = {1285196},
+ publisher = {Elsevier Science Inc.},
+ address = {New York, NY, USA},
+ keywords = {DENFIS, Image complexity, LSB matching, SVMRFE, Steganalysis},
+} 
+
+
+
+@InProceedings{LHS08,
+  author =      {Bin Li and Jiwu Huang and Yun Q. Shi},
+  title =       {Textural features based universal steganalysis},
+  OPTcrossref =  {},
+  OPTkey =      {},
+  booktitle = {Proc. SPIE 6819},
+  pages =       {12},
+  year =        {2008},
+  OPTeditor =   {},
+  OPTvolume =   {6819},
+
+  OPTseries =   {},
+  OPTaddress =          {},
+  month =       feb,
+  OPTorganization = {},
+  OPTpublisher = {},
+  OPTnote =     {},
+  OPTannote =   {}
+}
+
+
+@InProceedings{KF11,
+    author = {Jan Kodovský and Jessica Fridrich},
+    title = {Steganalysis in high dimensions: Fusing classifiers built on random subspaces},
+  OPTcrossref =  {},
+  OPTkey =      {},
+  booktitle = { Proc. SPIE, Electronic Imaging, Media Watermarking, Security, and Forensics XIII},
+  OPTpages =    {},
+  year =        {2011},
+  OPTeditor =   {},
+  OPTvolume =   {},
+  OPTnumber =   {},
+  OPTseries =   {},
+  OPTaddress =          {},
+  OPTmonth =    {},
+  OPTorganization = {},
+  OPTpublisher = {},
+  OPTnote =     {},
+  OPTannote =   {}
+}
+
+@article{DBLP:journals/tifs/KodovskyFH12,
+  author    = {Jan Kodovsk{\'y} and
+               Jessica J. Fridrich and
+               Vojtech Holub},
+  title     = {Ensemble Classifiers for Steganalysis of Digital Media},
+  journal   = {IEEE Transactions on Information Forensics and Security},
+  volume    = {7},
+  number    = {2},
+  year      = {2012},
+  pages     = {432-444},
+  ee        = {http://dx.doi.org/10.1109/TIFS.2011.2175919},
+  bibsource = {DBLP, http://dblp.uni-trier.de}
+}
+
+
+
+@article{Fillatre:2012:ASL:2333143.2333587,
+ author = {Fillatre, Lionel},
+ title = {Adaptive Steganalysis of Least Significant Bit Replacement in Grayscale Natural Images},
+ journal = {Trans. Sig. Proc.},
+ issue_date = {February 2012},
+ volume = {60},
+ number = {2},
+ month = feb,
+ year = {2012},
+ issn = {1053-587X},
+ pages = {556--569},
+ numpages = {14},
+ url = {http://dx.doi.org/10.1109/TSP.2011.2174231},
+ doi = {10.1109/TSP.2011.2174231},
+ acmid = {2333587},
+ publisher = {IEEE Press},
+ address = {Piscataway, NJ, USA},
+} 
\ No newline at end of file

diff --git a/intro.tex b/intro.tex
index f8974ceecc9305c17292b9b0b68d9985cc81eba0..dd2457095000f0db30d6ddd869719378415cfe6b 100644 (file)
--- a/intro.tex
+++ b/intro.tex
@@ -16,13 +16,27 @@ cannot be applied there to discover stego-images in LSBM.
 The most accurate detectors for this matching are universal steganalysers such as~\cite{LHS08,DBLP:conf/ih/2005,FK12}
 which classify images thanks to extracted features from neighboring elements of noise residual.  
 
 The most accurate detectors for this matching are universal steganalysers such as~\cite{LHS08,DBLP:conf/ih/2005,FK12}
 which classify images thanks to extracted features from neighboring elements of noise residual.  
 

+

 LSB matching revisited (LSBMR)~\cite{Mielikainen06} have been recently introduced. 
 LSB matching revisited (LSBMR)~\cite{Mielikainen06} have been recently introduced. 

-This scheme deals with pairs of pixels instead of individual ones.
-It thus allows to decrease the number of modified bits per cover pixel 
-for the same payload compared to LSB replacement and LSBM and 
-and avoids the LSB replacement style asymmetry. Unfortunately, 
-detectors referenced above are able to distinguish between 
-stego content images and  cover images.  
+For a given pair of pixels, in which the LSB
+of the first pixel carries one bit of secret message, and the relationship
+(odd–even combination) of the two pixel values carries
+another bit of secret message. 
+
+
+
+In such a way, the modification
+rate of pixels can decrease from 0.5 to 0.375 bits/pixel
+(bpp) in the case of a maximum embedding rate, meaning fewer
+changes to the cover image at the same payload compared to
+LSB replacement and LSBM. It is also shown that such a new
+scheme can avoid the LSB replacement style asymmetry, and
+thus it should make the detection slightly more difficult than the
+LSBM approach based on our experiments
+
+
+
+

 
 Instead of (efficiently) modifying LSBs, there is also a need to select pixels whose value 
 modification minimizes a distortion function.
 
 Instead of (efficiently) modifying LSBs, there is also a need to select pixels whose value 
 modification minimizes a distortion function.


@@ -69,7 +83,7 @@ Section~\ref{sec:experiments} shows experiments on image quality, steganalytic e
 and compare them to state of the art steganographic schemes.
 Finally, concluding notes and future works are given in section~\ref{sec:concl}
 
 and compare them to state of the art steganographic schemes.
 Finally, concluding notes and future works are given in section~\ref{sec:concl}
 

-theory : ?
+

 
 
 
 
 
 

diff --git a/main.tex b/main.tex
index fcbcdbbe98dcdf9c473269a00b7bb75908e05bce..e68d1cab7d2d47b6af97b80fb8264960993aa275 100755 (executable)
--- a/main.tex
+++ b/main.tex
@@ -1,4 +1,4 @@
-\documentclass[draft,journal]{IEEEtran}
+\documentclass[journal]{IEEEtran}

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


@@ -8,20 +8,8 @@
 
 
 \begin{document}
 
 
 \begin{document}

-%
-% paper title
-% can use linebreaks \\ within to get better formatting as desired
-\title{Bare Demo of IEEEtran.cls for Journals}
-%
-%
-% author names and IEEE memberships
-% note positions of commas and nonbreaking spaces ( ~ ) LaTeX will not break
-% a structure at a ~ so this keeps an author's name from being broken across
-% two lines.
-% use \thanks{} to gain access to the first footnote area
-% a separate \thanks must be used for each paragraph as LaTeX2e's \thanks
-% was not built to handle multiple paragraphs
-%
+\title{STABYLO: STeganography with cAnny, Bbs, binarY embedding at LOw cost}
+

 
 \author{Jean-Fran\c cois Couchot, Raphael Couturier, and  Christophe Guyeux*
   FEMTO-ST Institute, UMR 6174 CNRS\\
 
 \author{Jean-Fran\c cois Couchot, Raphael Couturier, and  Christophe Guyeux*
   FEMTO-ST Institute, UMR 6174 CNRS\\


@@ -33,13 +21,9 @@
 }
 \newcommand{\JFC}[1]{\begin{color}{green}\textit{#1}\end{color}}
 % make the title area
 }
 \newcommand{\JFC}[1]{\begin{color}{green}\textit{#1}\end{color}}
 % make the title area

-%\maketitle
+\maketitle

 
 
 
 

-\begin{abstract}
-%\boldmath
-The abstract goes here.
-\end{abstract}

 
 \begin{IEEEkeywords}
 %IEEEtran, journal, \LaTeX, paper, template.
 
 \begin{IEEEkeywords}
 %IEEEtran, journal, \LaTeX, paper, template.


@@ -54,61 +38,21 @@ Steganography, least-significant-bit (LSB)-based steganography, edge detection,
 
 
 
 
 
 

-\section{Introduction}
+\section{Introduction}\label{sec:intro}

 \input{intro.tex}
 
 \input{intro.tex}
 

-\section{Analysis of Steganographic Approaches}
-\input{analysis.tex}
-
-This work considers digital images as covers and fondation is
-spatial least significant-bit (LSB) replacement.
-I this data hiding scheme a subset of all the LSB of the cover image is modified 
-with a secret bit stream depending on to a key, the cover, and the message to embed.
-This well studied steganographic approach  never decreases (resp. increases)
-pixel with even value (resp. odd value) and may break structural symetry.
-This structure modification is detectable by statistical approaches 
-and thus by steganalysis methods~\cite{Dumitrescu:2005:LSB:1073170.1073176,DBLP:conf/ih/2005,FK12}. 
-
-
-random LSB selection: coarse, easily tractable, easilly detactable
-
-

 
 

-feature-based : precise but time consuming 
-
-security need : message has to be encrypted before beeing introduced. Using efficient and secured cryptographic approach
-
-LSBR: to coarse (apply hugo breaker on it)
-
-\section{Our Approach}
+\section{Our Approach}\label{sec:ourapproach}

 \input{ourapproach.tex}
 
 \input{ourapproach.tex}
 

-% Image Quality: Edge Image Steganography
+\section{Experiments}\label{sec:experiments}

 
 

-% Security aspect:
-% BBS-based cprotographic version of the message 

 
 

-% Enlarging embeding efficiency:
-% Syndrome treillis code  

 
 

-\input{stc.tex}
-\section{Conclusion}
+\section{Conclusion}\label{sec:concl}

 The conclusion goes here.
 
 
 The conclusion goes here.
 
 

-\appendices
-\section{Proof of the First Zonklar Equation}
-Appendix one text goes here.
-
-% you can choose not to have a title for an appendix
-% if you want by leaving the argument blank
-\section{}
-Appendix two text goes here.
-
-
-% use section* for acknowledgement
-\section*{Acknowledgment}
-The authors would like to thank...

 
 \bibliographystyle{plain}
 \bibliography{biblio}
 
 \bibliographystyle{plain}
 \bibliography{biblio}

diff --git a/ourapproach.tex b/ourapproach.tex
index 2bf476f6825fa8a647cbcd5749d1a34fedf23daf..28f826342d7d0423d7fe2db5a9b13ea4446ab8d0 100644 (file)
--- a/ourapproach.tex
+++ b/ourapproach.tex
@@ -33,22 +33,10 @@ and inside the extraction one(Fig.~\ref{fig:sch:ext}).
 
 \subsection{Steganalysis}
 
 
 \subsection{Steganalysis}
 

-LSB : 
-"Adaptive steganalysis of Least Significant Bit replacement in grayscale natural images"

 
 

-Structural LSB Detectors:
-\verb+http://dde.binghamton.edu/download/structural_lsb_detectors/+
+Détailler \cite{Fillatre:2012:ASL:2333143.2333587}

 
 

-Vainqueur du BOSS challenge 
-
-
-ensemble:
-
-
-G. Gül and F. Kurugollu. A new methodology in steganalysis : Breaking highly
-undetactable steganograpy (HUGO). In Information Hiding, 13th International
-Workshop, volume 6958 of LNCS, pages 71–84, Prague, Czech Republic, May 18–
-20, 2011. 
+Vainqueur du BOSS challenge~\cite{DBLP:journals/tifs/KodovskyFH12}

 
 
 \subsection{Data Embedding} 
 
 
 \subsection{Data Embedding} 


@@ -60,7 +48,7 @@ Workshop, volume 6958 of LNCS, pages 71–84, Prague, Czech Republic, May 18–
 Image Quality: Edge Image Steganography
 \JFC{Raphael, les fuzzy edge detection sont souvent utilisés. 
   il faudrait comparer les approches en terme de nombre de bits retournés,
 Image Quality: Edge Image Steganography
 \JFC{Raphael, les fuzzy edge detection sont souvent utilisés. 
   il faudrait comparer les approches en terme de nombre de bits retournés,

-  en terme de complexité. }
+  en terme de complexité. } \cite{KF11}

 
 
 Presentation des algos de detection de contour
 
 
 Presentation des algos de detection de contour

diff --git a/stc.tex b/stc.tex
index 6570c73bcbc08ea0b1362228b27955eb7bd1ca4c..e8c695cf1a3963369fa61f56df0c1abe0b4d369f 100644 (file)
--- a/stc.tex
+++ b/stc.tex
@@ -58,10 +58,7 @@ $2^n-1$ pixels needs $1-1/2^n$ average changes.
 
 
 
 
 
 

-Unfortunately, 
-
-
-for any given $H$, finding $y$ that solves $Hy=m$ and that 
+Unfortunately, for any given $H$, finding $y$ that solves $Hy=m$ and that 

 that minimizes $D_X(x,y)$ has exponential complexity with respect to $n$. 
 The Syndrome-Trellis Codes  (STC) 
 presented by Filler et al. in~\cite{DBLP:conf/mediaforensics/FillerJF10} 
 that minimizes $D_X(x,y)$ has exponential complexity with respect to $n$. 
 The Syndrome-Trellis Codes  (STC) 
 presented by Filler et al. in~\cite{DBLP:conf/mediaforensics/FillerJF10}