comparaison avec HUGO et autres

[canny.git] / intro.tex

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--- a/intro.tex
+++ b/intro.tex
@@ -1,12 +1,12 @@
 
 

-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.

-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.
+In this data hiding scheme a subset of all the LSB 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.
 These structural modification can be detected  by statistical approaches 
 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 

-and thus by steganalysis methods~\cite{DBLP:journals/tsp/DumitrescuWW03,DBLP:conf/mmsec/FridrichGD01,Dumitrescu:2005:LSB:1073170.1073176}
+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 
 
 This drawback is avoided in LSB matching (LSBM) where
 the $+1$ or $-1$ is randomly added to the cover pixel LSB value 


@@ -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.


@@ -35,33 +49,55 @@ features of SPAM computed from the cover and from the stego images.
 Thanks to this feature set, HUGO allows to embed $7\times$ longer messages with the same level of 
 indetectability than LSB matching. 
 However, this improvement is time consuming, mainly due to the distortion function
 Thanks to this feature set, HUGO allows to embed $7\times$ longer messages with the same level of 
 indetectability than LSB matching. 
 However, this improvement is time consuming, mainly due to the distortion function

-computation.
+computation. 
+

 
 There remains a large place between random selection of LSB and feature based modification of pixel values.
 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:
 
 There remains a large place between random selection of LSB and feature based modification of pixel values.
 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 
-pixels. In other words, minor changes in regular area is more dramatic than larger modifications in edge ones. 
+in cover image, edge pixels already break its continuity  and thus already contain large variation with neighbouring 
+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. 
 
 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.
 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}.
+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.
 We thus propose to take benefit of these optimized embedding, provided it is not too time consuming.

-Experiments have confirmed such a fact\JFC{Raphael....}. 
+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 
+is to enlarge the set of modified bits to increase the payload. 
+
+
+But, one can notice that all the previous referenced 
+schemes~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10,DBLP:conf/ih/PevnyFB10}
+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 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 
+cover signal.
+

  
 
 \JFC{Christophe : énoncer la problématique du besoin de crypto et de ``cryptographiquement sûr'',  les algo déjà cassés....
 l'efficacité d'un encodage/décodage ...} 
  
 
 \JFC{Christophe : énoncer la problématique du besoin de crypto et de ``cryptographiquement sûr'',  les algo déjà cassés....
 l'efficacité d'un encodage/décodage ...} 

-To deal with security issues, message is encrypted 
+To deal with security issues, message is encrypted... 

 
 

-In this paper, we thus propose to combine tried and tested techniques of signal theory (the adaptive edge detection), coding (the binary embedding), and cryptography 
-(the encrypt the message) to compute an efficient steganography scheme that is amenable to be executed on small devices.  
+In this paper, we thus propose to combine tried and 
+tested techniques of signal theory (the adaptive edge detection), coding (the binary embedding), and cryptography 
+(the encrypt the message) to compute an efficient steganography 
+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. 
 Section~\ref{sec:ourapproach} presents the details of our steganographic scheme.
 
 The rest of the paper is organised as follows. 
 Section~\ref{sec:ourapproach} presents the details of our steganographic scheme.


@@ -69,7 +105,7 @@ 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 : ?
+