X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/3c3e9a12f31ac42d0c3e7eb1be25aeafb7f7b3db..93faa138ef3fae7589a5dc3ba0d8862f7b54c4cb:/intro.tex?ds=sidebyside

diff --git a/intro.tex b/intro.tex
index a8433e5..deecdb7 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.  
 
 
@@ -72,7 +73,7 @@ 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 
+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
@@ -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,
@@ -113,14 +115,14 @@ Doing so makes our steganographic protocol, to a certain extend, an asymmetric o
 
 To sum up, in this research work, well studied and experimented
 techniques of signal processing (adaptive edges detection), 
-coding theory (syndrome-treillis codes), and cryptography 
+coding theory (syndrome-trellis codes), and cryptography 
 (Blum-Goldwasser encryption protocol) are combined 
 to compute an efficient steganographic
 scheme, whose principal characteristic is to take into 
 consideration the cover image and to be compatible with small computation resources.  
 
 The remainder of this document is organized as follows. 
-Section~\ref{sec:ourapproach} presents the details of the proposed steganographic scheme.
+Section~\ref{sec:ourapproach} presents the details of the proposed steganographic scheme and applies it on a running example.
 Section~\ref{sec:experiments} shows experiments on image quality, steganalytic evaluation, complexity of our approach,
 and compares it to the state of the art steganographic schemes.
 Finally, concluding notes and future work are given in Section~\ref{sec:concl}.
