X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/03f2db2f776786ee6e9435d3c0f603b248cdd2ae..01ec38902455e4ee4dba09ead751639638481fd2:/intro.tex?ds=sidebyside

diff --git a/intro.tex b/intro.tex
index 167b7dd..cd2aeea 100644
--- a/intro.tex
+++ b/intro.tex
@@ -45,18 +45,30 @@ LSBM approach. % based on our experiments
 
 Additionally to (efficiently) modifying LSBs, there is also a need to select pixels whose value 
 modification minimizes a distortion function.
-This distortion may be computed thanks to feature vectors that are embedded for instance in the steganalysers
+This distortion may be computed thanks to feature vectors that
+ are embedded for instance in the steganalysers
 referenced above. 
-The Highly Undetectable steGO (HUGO) method~\cite{DBLP:conf/ih/PevnyFB10} is one of the most efficient instance of such a scheme.
-It takes into account so-called SPAM features 
-%(whose size is larger than $10^7$) 
-to avoid over-fitting a particular 
-steganalyser. Thus a distortion measure for each pixel is individually determined as the sum of the differences between
+The Highly Undetectable steGO (HUGO) method~\cite{DBLP:conf/ih/PevnyFB10},
+WOW~\cite{conf/wifs/HolubF12}, and UNIWARD~\cite{HFD14}
+are some of the  most efficient instances of such a scheme.
+
+HUGO takes into account so-called SPAM features.
+Thus a distortion measure for each
+pixel is individually determined as the sum of the differences between
 the features of the SPAM computed from the cover and from the stego images.
-Due to this features set, HUGO allows to embed messages that are $7$ times longer than the former ones with the same level of 
+The features embedded in WOW and UNIWARD are based on Wavelet-based 
+directional filter. Thus, similarly, the distortion function is 
+the sum  of the differences between these wavelet coefficients  
+computed from the cover and from the stego images.
+
+
+Due to this distortion measures, HUGO, WOW and UNIWARD allow
+to embed messages that are $7$ times longer than the former
+ones with the same level of 
 indetectability as LSB matching. 
-However, this improvement is time consuming, mainly due to the distortion function
-computation. 
+However, this improvement has a larger computation cost, mainly due to
+ the distortion function
+calculus. 
 
 
 There remains a large place between random selection of LSB and feature based modification of pixel values.
@@ -96,10 +108,27 @@ One can notice that all the previously referenced
 sche\-mes~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10,DBLP:conf/ih/PevnyFB10}
 produce stego contents 
 by only considering the payload, not the type of image signal: the higher the payload is, 
-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 signals.
-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. 
+the better the approach is said to be.
+For instance, studied payloads range from 0.04 to 0.4 modified bits per pixel.
+Contrarily, we argue that some images should not be taken 
+as a cover because of the nature of their signals.
+Consider for instance a uniformly black image: a very tiny modification of its 
+pixels can be easily detectable.
+Practically speaking, if Alice would send
+a hidden message to Bob, she would never consider
+such kind of image and a high embedding rate.   
+The approach we propose here is thus to provide a small complexity 
+self adaptive algorithm
+with an acceptable payload, which 
+depends on the  cover signal. 
+The payload is further said to  
+ be acceptable if it  allows to embed a sufficiently 
+long message in the cover signal. 
+Practically speaking, our approach is efficient enough for
+payloads close to 0.06 bit per pixel which allows to embed 
+messages of length larger than 16000 bits in an 
+image of size $512\times 512$ pixels. 
+
 % 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,
@@ -126,7 +155,7 @@ The remainder of this document is organized as follows.
 Section~\ref{sec:ourapproach} presents the details of the proposed steganographic scheme and applies it on a running example. Among its technical description, 
 its adaptive aspect is emphasized.
 Section~\ref{sub:complexity} presents the overall complexity of our approach
-and compares it to the HUGO's one.
+and compares it to HUGO, WOW, and UNIWARD.
 Section~\ref{sec:experiments} shows experiments on image quality, steganalysis evaluation, and compares them to the state of the art steganographic schemes.
 Finally, concluding notes and future work are given in Section~\ref{sec:concl}.