X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/3c3e9a12f31ac42d0c3e7eb1be25aeafb7f7b3db..b417a74f270da1ad1a8b11552a8508c45cb75085:/intro.tex

diff --git a/intro.tex b/intro.tex
index a8433e5..8ff5f41 100644
--- a/intro.tex
+++ b/intro.tex
@@ -1,4 +1,3 @@
-
 This research work takes place in the field of information hiding, considerably developed
 these last two decades. The proposed method for 
 steganography considers digital images as covers.
@@ -7,7 +6,9 @@ of spatial least significant bits (LSBs) replacement schemes.
 Let us recall that, in this LSBR category, a subset of all the LSBs of the cover image is modified 
 with a secret bit stream depending on: a secret key, the cover, and the message to embed.
 In this well studied steganographic approach,
-pixels with even values (resp. odd values) are never decreased (resp. increased), 
+if we consider that a LSB is the last bit of each pixel value,  
+pixels with an even value (resp. an odd value) 
+are never decreased (resp. increased), 
 thus such schemes may break the 
 structural symmetry of the host images.
 And these structural alterations can be detected by 
@@ -20,7 +21,7 @@ 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.  
 
 
@@ -97,13 +98,14 @@ by only considering the payload, not the type of image signal: the higher the pa
 the better the approach is said to be. 
 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 
-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 which are modified. 
-% Finally, even if the steganalysis discipline
-% has done great leaps forward these last years, it is currently impossible to prove rigorously
+The approach we propose is thus to provide a self adaptive algorithm with a high payload, which depends on the  cover signal. 
+% Message extraction is achieved by computing the same
+% edge detection pixels set for the cover and the stego image. 
+% The edge detection algorithm is thus not applied  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,