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[canny.git] / intro.tex

diff --git a/intro.tex b/intro.tex
index ccdad7553ea62011f2c5604748df74a925531391..50c4bbec57581cdb49f46b5689e2fb6b03e4672b 100644 (file)
--- a/intro.tex
+++ b/intro.tex
@@ -25,7 +25,7 @@ The most accurate detectors for this matching are universal steganalysers such a
 which classify images according to extracted features from neighboring elements of residual noise.  
 
 
 which classify images according to extracted features from neighboring elements of residual noise.  
 
 

-Finally, LSB matching revisited (LSBMR) has been recently introduced in~\cite{Mielikainen06}. 
+Finally, LSB matching revisited (LSBMR) has recently been introduced in~\cite{Mielikainen06}. 

 It works as follows: for a given pair of pixels, the LSB
 of the first pixel carries a first bit of the secret message, while the parity relationship
 (odd/even combination) of the two pixel values carries
 It works as follows: for a given pair of pixels, the LSB
 of the first pixel carries a first bit of the secret message, while the parity relationship
 (odd/even combination) of the two pixel values carries


@@ -45,7 +45,7 @@ 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.

-This distortion may be computed thanks to feature vectors that are embedded for instance in steganalysers
+This distortion may be computed thanks to feature vectors that are embedded for instance in the steganalysers

 referenced above. 
 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 
 referenced above. 
 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 


@@ -53,8 +53,8 @@ It takes into account so-called SPAM features
 to avoid overfitting a particular 
 steganalyser. 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.
 to avoid overfitting a particular 
 steganalyser. 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.

-Thanks to this features set, HUGO allows to embed $7\times$ longer messages with the same level of 
-indetectability than LSB matching. 
+Thanks to this features set, HUGO allows 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 is time consuming, mainly due to the distortion function
 computation. 
 


@@ -73,17 +73,17 @@ 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 present the Edge Adaptive
 approaches being detailed in~\cite{Luo:2010:EAI:1824719.1824720} and 
 in~\cite{DBLP:journals/eswa/ChenCL10}.
 In the former, the authors present the Edge Adaptive

-Image Steganography based on LSB matching revisited further denoted as to 
+Image Steganography based on LSB matching revisited further denoted as  

 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.
 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. 
+Then the data hiding algorithm is achieved by applying LSBMR on some of the pixels of these regions. 

 The authors show that their proposed method is more efficient than all the LSB, LSBM, and LSBMR approaches 
 thanks to extensive experiments.
 However, it has been shown that the distinguishing error with LSB embedding is lower than 
 the one with some binary embedding~\cite{DBLP:journals/tifs/FillerJF11}.
 The authors show that their proposed method is more efficient than all the LSB, LSBM, and LSBMR approaches 
 thanks to extensive experiments.
 However, it has been shown that the distinguishing error with LSB embedding is lower than 
 the one with some binary embedding~\cite{DBLP:journals/tifs/FillerJF11}.

-We thus propose to take benefit of these optimized embeddings, provided they are not too time consuming.
+We thus propose to take advantage of these optimized embeddings, provided they are not too time consuming.

 In the latter, an hybrid edge detector is presented followed by an ad hoc
 embedding. 
 The Edge detection is computed by combining fuzzy logic~\cite{Tyan1993} 
 In the latter, an hybrid edge detector is presented followed by an ad hoc
 embedding. 
 The Edge detection is computed by combining fuzzy logic~\cite{Tyan1993} 


@@ -105,7 +105,7 @@ The approach we propose is thus to provide a self adaptive algorithm with a high
 % but to exclude the LSBs which are modified. 
 
 Finally, even if the steganalysis discipline
 % 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
+ has known  great innovations 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,
 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,