X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/1bfb9cc8f38edd065a52de421a0dc41567c4262c..f4086cd758c530e5056b03d442f1cb5cf07b447b:/intro.tex

diff --git a/intro.tex b/intro.tex
index f1db772..71eebb9 100644
--- a/intro.tex
+++ b/intro.tex
@@ -20,7 +20,7 @@ only if this one does not correspond to the secret bit.
 %TODO : modifier ceci
 Since it is possible to make that probabilities of increasing or decreasing the pixel value, for instance by considering well encrypted hidden messages, usual statistical approaches 
 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/2005,FK12},
+The most accurate detectors for this matching are universal steganalysers such as~\cite{LHS08,DBLP:conf/ih/Ker05,FK12},
 which classify images according to extracted features from neighboring elements of residual noise.  
 
 
@@ -67,9 +67,14 @@ in a cover image, edges already break the  continuity of pixels' intensity map a
 pixels. In other words, minor changes in regular areas are more dramatic than larger modifications in edge ones. 
 Our first proposal is thus to embed message bits into edge shapes while preserving other smooth regions. 
 
-Edge based steganographic schemes have already been  studied, the most interesting 
-approaches being detailed in~\cite{Luo:2010:EAI:1824719.1824720} and \cite{DBLP:journals/eswa/ChenCL10}.
-In the former, the authors show how to select sharper edge regions with respect 
+Edge based steganographic schemes have already been  studied, 
+the most interesting 
+approaches being detailed in~\cite{Luo:2010:EAI:1824719.1824720} and 
+in~\cite{DBLP:journals/eswa/ChenCL10}.
+In the former, the authors presents the Edge Adaptive
+Image Steganography based on lsb matching revisited further denoted as to 
+EAISLSBMR. This approach selects sharper edge
+ regions with respect 
 to a given 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 these regions. 
@@ -96,14 +101,14 @@ The approach we propose is thus to provide a self adaptive algorithm with a high
 cover signal. 
 
 Additionally, in the steganographic context, the data hiding procedure is often required
-to be a reversible one. We thus need to be able to compute the same edge detection pixels 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 to exclude the LSBs. 
-Finally, even if the steganalysis discipline
-has done great leaps forward these last years, it is currently impossible to prove rigorously
+to be a reversible one. We thus need to be able to compute the same edge detection pixels 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 to exclude the LSBs which are modified. 
+% Finally, even if the steganalysis discipline
+% has done great leaps forward these last years, it is currently impossible to prove rigorously
 that a given hidden message cannot be recovered by an attacker.
 This is why we add to our scheme a reasonable
 message encryption stage, to be certain that,
 even in the worst case scenario, the attacker
-will not be able to obtain the message content.
+will not be able to obtain the original message content.
 Doing so makes our steganographic protocol, to a certain extend, an asymmetric one.
 
 To sum up, in this research work, well studied and experimented
