Merge branch 'master' of ssh://info.iut-bm.univ-fcomte.fr/canny

[canny.git] / intro.tex

diff --git a/intro.tex b/intro.tex
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+++ b/intro.tex
@@ -1,9 +1,8 @@
-This research work takes place in the field of information hiding, considerably developed
-these last two decades. The proposed method for 
+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.
 It belongs to the well-known large category
 of spatial least significant bits (LSBs) replacement schemes.
 steganography considers digital images as covers.
 It belongs to the well-known large category
 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 
+Let us recall that, in this LSB replacement 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,
 if we consider that a LSB is the last bit of each pixel value,  
 with a secret bit stream depending on: a secret key, the cover, and the message to embed.
 In this well-studied steganographic approach,
 if we consider that a LSB is the last bit of each pixel value,  


@@ -12,14 +11,15 @@ 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 
 thus such schemes may break the 
 structural symmetry of the host images.
 And these structural alterations can be detected by 

-well-designed statistical investigations, leading to known steganalysis methods~\cite{DBLP:journals/tsp/DumitrescuWW03,DBLP:conf/mmsec/FridrichGD01,Dumitrescu:2005:LSB:1073170.1073176}.
+well-designed statistical investigations, leading to well-known 
+steganalysis methods~\cite{DBLP:journals/tsp/DumitrescuWW03,DBLP:conf/mmsec/FridrichGD01,Dumitrescu:2005:LSB:1073170.1073176}.

 
 Let us recall too that this drawback 
 
 Let us recall too that this drawback 

-can be corrected considering the LSB matching (LSBM) subcategory, in which
-the $+1$ or $-1$ is randomly added to the cover pixel LSB value 
+can be fixed by considering the LSB matching (LSBM) subcategory, in which
+a $+1$ or $-1$ is randomly added to the cover pixel's LSB value 

 only if this one does not correspond to the secret bit.
 %TODO : modifier ceci
 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 
+By considering well-encrypted hidden messages, the probabilities of increasing or decreasing the value of pixels  are equal. Then 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/Ker05,FK12},
 which classify images according to extracted features from neighboring elements of residual noise.  
 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/Ker05,FK12},
 which classify images according to extracted features from neighboring elements of residual noise.  


@@ -43,20 +43,30 @@ LSBM approach. % based on our experiments
 
 
 
 
 
 

-Instead of (efficiently) modifying LSBs, there is also a need to select pixels whose value 
+Additionally to (efficiently) modifying LSBs, there is also a need to select pixels whose value 

 modification minimizes a distortion function.
 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. 
 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 
-%(whose size is larger than $10^7$) 
-to avoid overfitting 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.
 the features of the SPAM computed from the cover and from the stego images.

-Thanks 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. 
 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
+computation.

 
 
 There remains a large place between random selection of LSB and feature based modification of pixel values.
 
 
 There remains a large place between random selection of LSB and feature based modification of pixel values.


@@ -76,29 +86,47 @@ In the former, the authors present the Edge Adaptive
 Image Steganography based on LSB matching revisited further denoted as  
 EAISLSBMR. This approach selects sharper edge
  regions with respect 
 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
+to a given embedding rate: the larger the number of bits to be embedded is, the coarser

 the edge regions are.
 the edge regions are.

-Then the data hiding algorithm is achieved by applying LSBMR on some of the pixels of these regions. 
+Then the data hiding algorithm is achie\-ved 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 
 The authors show that their proposed method is more efficient than all the LSB, LSBM, and LSBMR approaches 

-thanks to extensive experiments.
+through 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}.
 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 advantage of these optimized embeddings, provided they are not too time consuming.
+We thus propose to take advantage of this optimized embedding, provided they are not too time consuming.

 In the latter, an hybrid edge detector is presented followed by an ad hoc
 embedding. 
 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} 
-and Canny~\cite{Canny:1986:CAE:11274.11275} approaches. The goal of this combination 
+The Edge detection is computed by combining fuz\-zy logic~\cite{Tyan1993} 
+and Canny~\cite{Canny:1986:CAE:11274.11275} approaches. 
+The goal of this combination 

 is to enlarge the set of modified bits to increase the payload of the data hiding scheme. 
 
 
 One can notice that all the previously referenced 
 is to enlarge the set of modified bits to increase the payload of the data hiding scheme. 
 
 
 One can notice that all the previously referenced 

-schemes~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10,DBLP:conf/ih/PevnyFB10}
+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, 
 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 detected.
+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 15,728 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,
 % 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,


@@ -113,21 +141,21 @@ even in the worst case scenario, the attacker
 will not be able to obtain the original message content.
 Doing so makes our steganographic protocol, to a certain extend, an asymmetric one.
 
 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
+To sum up, well-studied and experimented

 techniques of signal processing (adaptive edges detection), 
 coding theory (syndrome-trellis codes), and cryptography 
 techniques of signal processing (adaptive edges detection), 
 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 
+(Blum-Goldwas\-ser encryption protocol) are combined in this research work.
+The objective is to  compute an efficient steganographic
+sche\-me, 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. 
 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 and applies it on a running example.
-Section~\ref{sec:experiments} shows experiments on image quality, steganalytic evaluation, complexity of our approach,
-and compare them to the state of the art steganographic schemes.
+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 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}.
 
 
 
 Finally, concluding notes and future work are given in Section~\ref{sec:concl}.
 
 
 

-
-