reprise intro

[canny.git] / intro.tex

diff --git a/intro.tex b/intro.tex
index 751fae8e095f2e1476df56e566e11fc61f49cd6a..cd2aeea16a1ed0cf355eb5ece703a5a1668ddf0e 100644 (file)
--- a/intro.tex
+++ b/intro.tex
@@ -1,4 +1,4 @@
-This research work takes place in the field of information hiding, considerably developed for the 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.


@@ -11,15 +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 well 
-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
+the $+1$ 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

-By considering well-encrypted hidden messages, the probabilities of increasing or decreasing the alue of pixels  are equal. Then 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 +43,32 @@ 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. 

-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 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
+calculus. 

 
 
 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.


@@ -78,12 +90,12 @@ EAISLSBMR. This approach selects sharper edge
  regions with respect 
 to a given embedding rate: the larger the number of bits to be embedded is, the coarser
 the edge regions are.
  regions with respect 
 to a given embedding rate: the larger the number of bits to be embedded is, the coarser
 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. 
 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} 


@@ -93,13 +105,30 @@ is to enlarge the set of modified bits to increase the payload of the data hidin
 
 
 One can notice that all the previously referenced 
 
 
 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 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,
 % 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,


@@ -126,11 +155,9 @@ 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
 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}.
 
 
 
 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}.
 
 
 

-
-