kl

diff --git a/experiments.tex b/experiments.tex
index 9af03c672885bd3557429e9dd2cc5f88c3dc0af5..776a5a5c8a39a68db06346fba4cc24d64d233958 100644 (file)
--- a/experiments.tex
+++ b/experiments.tex
@@ -5,6 +5,25 @@ grayscale digital image in a RAW format.
 We restrict experiments to 
 this set of cover images since this paper is more focused on 
 the methodology than benchmarking.    
 We restrict experiments to 
 this set of cover images since this paper is more focused on 
 the methodology than benchmarking.    

+We use the matrices given in table~\ref{table:matrices:H}
+as introduced in~\cite{}, since these ones have experimentally 
+be proven to have the best modification efficiency.
+
+\begin{table}
+$$
+\begin{array}{|l|l|}
+\textrm{rate} & \textrm{matrix generators} \\
+$\frac{1}{2} & \{71,109\}
+$\frac{1}{3} & \{95, 101, 121\}
+$\frac{1}{4} & \{81, 95, 107, 121\}
+$\frac{1}{5} & \{75, 95, 97, 105, 117\}
+$\frac{1}{6} & \{73, 83, 95, 103, 109, 123\}
+$\frac{1}{7} & \{69, 77, 93, 107, 111, 115, 121\}
+$\frac{1}{8} & \{69, 79, 81, 89, 93, 99, 107, 119\}
+$\frac{1}{9} & \{69, 79, 81, 89, 93, 99, 107, 119, 125]
+
+
+

 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 least detectable information hiding tool in spatial domain 
 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 least detectable information hiding tool in spatial domain 

diff --git a/intro.tex b/intro.tex
index ff1a404acc0e8fb0e9e5bb1f111e3f7ccd065c71..cb4c669b200c17b9972b1a80b912464927f9c46f 100644 (file)
--- a/intro.tex
+++ b/intro.tex
@@ -19,9 +19,9 @@ can be corrected considering the LSB matching (LSBM) subcategory, in which
 the $+1$ or $-1$ is randomly added to the cover pixel LSB value 
 only if this one does not correspond to the secret bit.
 %TODO : modifier ceci
 the $+1$ or $-1$ is randomly added to the cover pixel LSB value 
 only if this one does not correspond to the secret bit.
 %TODO : modifier ceci

-By considering well-encrypted hidden messages, probabilities of increasing or ofdecreasing value of pixels  are equal. Then usual statistical approaches 
+By considering well-encrypted hidden messages, probabilities of increasing or of decreasing value of pixels  are equal. Then usual statistical approaches 

 cannot be applied here to discover stego-contents in LSBM.
 cannot be applied here to discover stego-contents in LSBM.

-The most accurate detectors for this matching are universal steganalysers such as~\cite{LHS08,DBLP:conf/ih/Ker05,FK12},
+The most accurate detectors for this matching are universal steganalysers such as~\cite{LS08,DBLP:conf/ih/Ker05,FK12},

 which classify images according to extracted features from neighboring elements of residual noise.  
 
 
 which classify images according to extracted features from neighboring elements of residual noise.  
 
 


@@ -122,9 +122,11 @@ scheme, whose principal characteristic is to take into
 consideration the cover image and to be compatible with small computation resources.  
 
 The remainder of this document is organized as follows. 
 consideration the cover image and to be compatible with small computation resources.  
 
 The remainder of this document is organized as follows. 

-Section~\ref{sec:ourapproach} presents the details of the proposed steganographic scheme and applies it on a running example.
-Section~\ref{sec:experiments} shows experiments on image quality, steganalytic evaluation, complexity of our approach,
-and compare them to the state of the art steganographic schemes.
+Section~\ref{sec:ourapproach} presents the details of the proposed steganographic scheme and applies it on a running example. Among its technical description, 
+its adaptive aspect is emphasized.
+Section~\ref{sub:complexity} presents the overall complexity of our approach
+and compare it to the HUGO's one.
+Section~\ref{sec:experiments} shows experiments on image quality, steganalysis evaluation, and compare them to the state of the art steganographic schemes.

 Finally, concluding notes and future work are given in Section~\ref{sec:concl}.
 
 
 Finally, concluding notes and future work are given in Section~\ref{sec:concl}.
 
 

diff --git a/main.tex b/main.tex
index 0f789bdf41352f8c25616b76de7c7d95aadb841a..272293e694d416abf5715736684da08a8376786c 100755 (executable)
--- a/main.tex
+++ b/main.tex
@@ -32,7 +32,8 @@
 
 
 \title[STABYLO]{STABYLO: 
 
 
 \title[STABYLO]{STABYLO: 

-a lightweight edge-based steganographic approach}
+STeganography with 
+Adaptive,  Bbs, and binarY embedding at LOw cost.}

 
 \author{Jean-Fran\c cois Couchot, Raphael Couturier, and  Christophe Guyeux\thanks{Authors in alphabetic order}}
 
 
 \author{Jean-Fran\c cois Couchot, Raphael Couturier, and  Christophe Guyeux\thanks{Authors in alphabetic order}}
 


@@ -104,14 +105,17 @@ a scheme that can reasonably face up-to-date steganalysers.
 \input{ourapproach.tex}
 
 
 \input{ourapproach.tex}
 
 

+\section{Complexity Analysis}\label{sub:complexity}
+\input{comlexity}
+

 \section{Experiments}\label{sec:experiments}
 \input{experiments}
 
 
 \section{Conclusion}\label{sec:concl}
 
 \section{Experiments}\label{sec:experiments}
 \input{experiments}
 
 
 \section{Conclusion}\label{sec:concl}
 

-The STABYLO algorithm, whose acronym means STeganography 
-with cAnny, Bbs, binarY embedding at LOw cost, has been introduced 
+The STABYLO algorithm, whose acronym means STeganography with 
+Adaptive,  Bbs, and 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.


@@ -121,22 +125,16 @@ pseudorandom number generator, together with Syndrome-Trellis Codes
 for minimizing distortion.
 After having introduced with details the proposed method,
 we have evaluated it through noise measures (namely, the PSNR, PSNR-HVS-M, 
 for minimizing distortion.
 After having introduced with details the proposed method,
 we have evaluated it through noise measures (namely, the PSNR, PSNR-HVS-M, 

-BIQI, and weighted PSNR) and we have used well-established steganalysers.
+BIQI, and weighted PSNR), we have used well-established steganalysers.

 
 % Of course, other detectors like the fuzzy edge methods
 % deserve much further attention, which is why we intend 
 % to investigate systematically all of these detectors in our next work.
 
 
 
 % Of course, other detectors like the fuzzy edge methods
 % deserve much further attention, which is why we intend 
 % to investigate systematically all of these detectors in our next work.
 
 

-c
-

 For future work, the authors' intention is to investigate systematically 
 all the existing edge detection methods, to see if the STABYLO evaluation scores can
 be improved by replacing Canny with another edge filter. 
 For future work, the authors' intention is to investigate systematically 
 all the existing edge detection methods, to see if the STABYLO evaluation scores can
 be improved by replacing Canny with another edge filter. 

-% We will try
-% to take into account the least significant bits too during all the
-% stages of the algorithm, hoping by doing so to be closer to the HUGO scores against 
-% steganalyzers. 

 Other steganalysers than the ones used in this document will be
 examined for the sake of completeness. Finally, the
 systematic replacement of all the LSBs of edges by binary digits provided
 Other steganalysers than the ones used in this document will be
 examined for the sake of completeness. Finally, the
 systematic replacement of all the LSBs of edges by binary digits provided

diff --git a/ourapproach.tex b/ourapproach.tex
index e87c1486bc31d3942abf9acd70e48c53077c7733..1b2ee1a619182b8899607356d17db4c7e2d74370 100644 (file)
--- a/ourapproach.tex
+++ b/ourapproach.tex
@@ -8,7 +8,8 @@ The message extraction is then presented  (Sect.~\ref{sub:extract}) and a runnin
 
 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 
+Adaptive, Bbs, binarY embedding at LOw cost.

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


@@ -372,35 +373,3 @@ This function allows to emphasize differences between contents.
 
 
 
 
 
 

-\section{Complexity Analysis}\label{sub:complexity}
-This section aims at justifying the leightweight attribute of our approach.
-To be more precise, we compare the complexity of our schemes to the 
-state of the art steganography, namely HUGO~\cite{DBLP:conf/ih/PevnyFB10}.
-
-
-In what folllows, we consider an $n \times n$ square image. 
-First of all, HUGO starts with computing the second order SPAM Features.
-This steps is in  $O(n^2 + 2.343^2)$ due to the calculation 
-of the difference arrays and next of the 686 features (of size 343).
-Next for each pixel, the distortion measure is calculated by +1/-1 modifying
-its value and computing again the SPAM 
-features. Pixels are thus selected according to their ability to provide
-an image whose SPAM features are close to the original one. 
-The algorithm is thus computing a distance between each Feature, 
-which is at least in $O(343)$ and an overall distance between these 
-metrics which is in $O(686)$. Computing the distance is thus in 
-$O(2\time 343^2)$ and this mdification is thus in $O(2\time 343^2 \time n^2)$.
-Ranking these results may be achieved with a insertion sort which is in $2.n^2 \ln(n)$.
-The overall complexity of the pixel selection is thus 
-$O(n^2 +2.343^2 + 2\time 343^2 \time n^2 + 2.n^2 \ln(n))$, \textit{i.e}
-$O(2.n^2(343^2 + \ln(n)))$.
-
-Our edge selection is based on a Canny  Filter, 
-whose complexity is in $O(2n^2.\ln(n))$ thanks to the convolution step
-which can be implemented with FFT.
-The complexity of Hugo is  at least $343^2/\ln{n}$ times higher than our scheme. 
-
-
-
-
-