typos

[canny.git] / intro.tex

diff --git a/intro.tex b/intro.tex
index f8974ceecc9305c17292b9b0b68d9985cc81eba0..54b59e22a035981495cfd38843b811de061865b6 100644 (file)
--- a/intro.tex
+++ b/intro.tex
@@ -1,12 +1,12 @@
 
 This work considers digital images as covers and foundation is
 spatial least significant-bit (LSB) replacement.
 
 This work considers digital images as covers and foundation is
 spatial least significant-bit (LSB) replacement.

-I this data hiding scheme a subset of all the LSB of the cover image is modified 
+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.
 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 
 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 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.


@@ -42,7 +56,7 @@ 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 
 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. 
+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}.
 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}.


@@ -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 : ?
+