relecture

[canny.git] / intro.tex

diff --git a/intro.tex b/intro.tex
index 16e8c8b6d6dd236433c3d4e770f263f79587878f..e6a3d97ebd6f8cfb799966f78b1c893d905870e4 100644 (file)
--- a/intro.tex
+++ b/intro.tex
@@ -1,28 +1,28 @@
 
 

-This work considers digital images as covers and foundation is
+This work considers digital images as covers and it is based on

 spatial least significant-bit (LSB) replacement.
 spatial least significant-bit (LSB) replacement.

-In 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.
+In this data hiding scheme a subset of all the LSBs of the cover image is modified 
+with a secret bit stream depending on 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 symmetry.
 This well studied steganographic approach  never decreases (resp. increases)
 pixel with even value (resp. odd value) and may break structural symmetry.

-These structural modification can be detected  by statistical approaches 
+These structural modifications can be detected  by statistical approaches 

 and thus by steganalysis methods~\cite{DBLP:journals/tsp/DumitrescuWW03,DBLP:conf/mmsec/FridrichGD01,Dumitrescu:2005:LSB:1073170.1073176}.
 
 This drawback is avoided in LSB matching (LSBM) where
 the $+1$ or $-1$ is randomly added to the cover pixel LSB value 
 only if this one does not match the secret bit.
 and thus by steganalysis methods~\cite{DBLP:journals/tsp/DumitrescuWW03,DBLP:conf/mmsec/FridrichGD01,Dumitrescu:2005:LSB:1073170.1073176}.
 
 This drawback is avoided in LSB matching (LSBM) where
 the $+1$ or $-1$ is randomly added to the cover pixel LSB value 
 only if this one does not match the secret bit.

-Since probabilities of increasing or decreasing pixel value are the same, statistical approaches 
-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}
+Since probabilities of increasing or decreasing the pixel value are the same, statistical approaches 
+cannot be applied here 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.  
 
 
 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) has been recently introduced in~\cite{Mielikainen06}. 

 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. 
 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. 

-
+\CG{Je pige pas cette phrase, et je comprends pas l'explication du LSBMR :/}

 
 
 In such a way, the modification
 
 
 In such a way, the modification


@@ -31,7 +31,7 @@ rate of pixels can decrease from 0.5 to 0.375 bits/pixel
 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
 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
+thus it should make the detection slightly more difficult than in the

 LSBM approach. % based on our experiments
 
 
 LSBM approach. % based on our experiments
 
 


@@ -56,20 +56,21 @@ There remains a large place between random selection of LSB and feature based mo
 We argue that modifying edge pixels is an acceptable compromise. 
 Edges form the outline of an object: they are the boundary between overlapping objects or between an object
 and the background. A small modification of pixel value in the stego image should not be harmful to the image quality:
 We argue that modifying edge pixels is an acceptable compromise. 
 Edges form the outline of an object: they are the boundary between overlapping objects or between an object
 and the background. A small modification of pixel value in the stego image should not be harmful to the image quality:

-in cover image, edge pixels already break its continuity  and thus already contains large variation with neighbouring 
+in cover image, edge pixels already break its continuity  and thus already contain large variation with neighboring 

 pixels. In other words, minor changes in regular area are more dramatic than larger modifications in edge ones. 
 Our proposal is thus to embed message bits into edge shapes while preserving other smooth regions. 
 
 pixels. In other words, minor changes in regular area are more dramatic than larger modifications in edge ones. 
 Our proposal is thus to embed message bits into edge shapes while preserving other smooth regions. 
 

-Edge based steganographic schemes have bee already studied~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10}.
+Edge based steganographic schemes have been already studied~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10}.

 In the former, the authors show how to select sharper edge regions with respect 
 In the former, the authors show how to select sharper edge regions with respect 

-to embedding rate: the larger the number of bits to be embedded, the coarse the edge regions are.
+to embedding rate: the larger the number of bits to be embedded, the coarser
+the edge regions are.

 Then the data hiding algorithm is achieved by applying LSBMR on pixels of this region. 
 The authors show that this method is more efficient than all the LSB, LSBM, LSBMR approaches 
 thanks to extensive experiments.
 However, it has been shown that the distinguish error with LSB embedding is fewer than 
 the one with some binary embedding~\cite{DBLP:journals/tifs/FillerJF11}.
 We thus propose to take benefit of these optimized embedding, provided it is not too time consuming.
 Then the data hiding algorithm is achieved by applying LSBMR on pixels of this region. 
 The authors show that this method is more efficient than all the LSB, LSBM, LSBMR approaches 
 thanks to extensive experiments.
 However, it has been shown that the distinguish error with LSB embedding is fewer than 
 the one with some binary embedding~\cite{DBLP:journals/tifs/FillerJF11}.
 We thus propose to take benefit of these optimized embedding, provided it is not too time consuming.

-In the latter, an hybrid edge detector is presented followed by an ad'hoc
+In the latter, an hybrid edge detector is presented followed by an ad hoc

 embedding approach. 
 The Edge detection is computed by combining fuzzy logic~\cite{Tyan1993} 
 and Canny~\cite{Canny:1986:CAE:11274.11275} approaches. The goal of this combination 
 embedding approach. 
 The Edge detection is computed by combining fuzzy logic~\cite{Tyan1993} 
 and Canny~\cite{Canny:1986:CAE:11274.11275} approaches. The goal of this combination 


@@ -81,11 +82,12 @@ schemes~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10,DBLP:conf
 produce stego content 
 with only considering the payload, not the type of image signal: the higher the payload is, 
 the better the approach is said to be. 
 produce stego content 
 with only considering the payload, not the type of image signal: the higher the payload is, 
 the better the approach is said to be. 

-Contrarely, we argue that some images should'nt be taken as a cover because of the nature of their signal.
+Contrarily, we argue that some images should not be taken as a cover because of the nature of their signal.

 Consider for instance a uniformly black image: a very tiny modification of its pixels can be easily detectable.  
 The approach we propose is thus to provide a self adaptive algorithm with a high payload, which depends on the 
 Consider for instance a uniformly black image: a very tiny modification of its pixels can be easily detectable.  
 The approach we propose is thus to provide a self adaptive algorithm with a high payload, which depends on the 

-cover signal.
+cover signal. 

 
 

+For some applications it might be interesting to be have a reversible procedure to compute the same edge detection pixel set for the cover and the stego image. For this, we propose to apply the edge detection algorithm not on all the bits of the image but only on all the bits without taking into consideration the LSB. 

  
 
 \JFC{Christophe : énoncer la problématique du besoin de crypto et de ``cryptographiquement sûr'',  les algo déjà cassés....
  
 
 \JFC{Christophe : énoncer la problématique du besoin de crypto et de ``cryptographiquement sûr'',  les algo déjà cassés....


@@ -98,7 +100,7 @@ tested techniques of signal theory (the adaptive edge detection), coding (the bi
 scheme, which takes into consideration the cover image 
 an which is amenable to be executed on small devices.  
 
 scheme, which takes into consideration the cover image 
 an which is amenable to be executed on small devices.  
 

-The rest of the paper is organised as follows. 
+The rest of the paper is organized as follows. 

 Section~\ref{sec:ourapproach} presents the details of our steganographic scheme.
 Section~\ref{sec:experiments} shows experiments on image quality, steganalytic evaluation, complexity of our approach 
 and compare them to state of the art steganographic schemes.
 Section~\ref{sec:ourapproach} presents the details of our steganographic scheme.
 Section~\ref{sec:experiments} shows experiments on image quality, steganalytic evaluation, complexity of our approach 
 and compare them to state of the art steganographic schemes.