new

diff --git a/experiments.tex b/experiments.tex
index aa8a3e94dd8b4c872185249d20ffcedeb6fd4c44..9af03c672885bd3557429e9dd2cc5f88c3dc0af5 100644 (file)
--- a/experiments.tex
+++ b/experiments.tex
@@ -1,4 +1,4 @@
-For whole experiments, the whole set of 10000 images 
+For whole experiments, the whole set of 10,000 images 

 of the BOSS contest~\cite{Boss10} database is taken. 
 In this set, each cover is a $512\times 512$
 grayscale digital image in a RAW format. 
 of the BOSS contest~\cite{Boss10} database is taken. 
 In this set, each cover is a $512\times 512$
 grayscale digital image in a RAW format. 


@@ -8,7 +8,7 @@ the methodology than benchmarking.
 Our approach is always compared to Hugo~\cite{DBLP:conf/ih/PevnyFB10}
 and to EAISLSBMR~\cite{Luo:2010:EAI:1824719.1824720}.
 The former is the least detectable information hiding tool in spatial domain 
 Our approach is always compared to Hugo~\cite{DBLP:conf/ih/PevnyFB10}
 and to EAISLSBMR~\cite{Luo:2010:EAI:1824719.1824720}.
 The former is the least detectable information hiding tool in spatial domain 

-and the later is the work that is close to ours, as far as we know. 
+and the latter is the work that is the closest to ours, as far as we know. 

 
 
 
 
 
 


@@ -16,7 +16,7 @@ First of all,  in our experiments and with the adaptive scheme,
 the average size of the message that can be embedded is 16,445 bits.
 Its corresponds to an  average payload of 6.35\%. 
 The two other tools will then be compared with this payload. 
 the average size of the message that can be embedded is 16,445 bits.
 Its corresponds to an  average payload of 6.35\%. 
 The two other tools will then be compared with this payload. 

-The Sections~\ref{sub:quality} and~\ref{sub:steg} respectively present 
+Sections~\ref{sub:quality} and~\ref{sub:steg} respectively present 

 the quality analysis and the security of our scheme. 
 
 
 the quality analysis and the security of our scheme. 
 
 


@@ -79,7 +79,7 @@ HUGO and STABYLO with  STC+adaptive parameters.
 Results are summarized in Table~\ref{table:quality}.
 Let us give an interpretation of these experiments.
 First of all, the adaptive strategy produces images with lower distortion 
 Results are summarized in Table~\ref{table:quality}.
 Let us give an interpretation of these experiments.
 First of all, the adaptive strategy produces images with lower distortion 

-than the one of images resulting from the 10\% fixed strategy.
+than the images resulting from the 10\% fixed strategy.

 Numerical results are indeed always greater for the former strategy than 
 for the latter one.
 These results are not surprising since the adaptive strategy aims at 
 Numerical results are indeed always greater for the former strategy than 
 for the latter one.
 These results are not surprising since the adaptive strategy aims at 


@@ -93,7 +93,7 @@ the two least significant bits.
 
 If we combine \emph{adaptive} and \emph{STC} strategies 
 (which leads to an average embedding rate equal to 6.35\%)
 
 If we combine \emph{adaptive} and \emph{STC} strategies 
 (which leads to an average embedding rate equal to 6.35\%)

-our approach  provides equivalent metrics than HUGO.
+our approach  provides metrics equivalent to those provided by HUGO.

 In this column STC(7) stands for embedding data in the LSB whereas
 in STC(6), data are hidden in the two last significant bits. 
 
 In this column STC(7) stands for embedding data in the LSB whereas
 in STC(6), data are hidden in the two last significant bits. 
 


@@ -122,7 +122,7 @@ The steganalysis quality of our approach has been evaluated through the two
 AUMP~\cite{Fillatre:2012:ASL:2333143.2333587}
 and Ensemble Classifier~\cite{DBLP:journals/tifs/KodovskyFH12} based steganalysers.
 Both aim at detecting hidden bits in grayscale natural images and are 
 AUMP~\cite{Fillatre:2012:ASL:2333143.2333587}
 and Ensemble Classifier~\cite{DBLP:journals/tifs/KodovskyFH12} based steganalysers.
 Both aim at detecting hidden bits in grayscale natural images and are 

-considered as the state of the art of steganalysers in spatial domain~\cite{FK12}.
+considered as state of the art steganalysers in the spatial domain~\cite{FK12}.

 The former approach is based on a simplified parametric model of natural images.
 Parameters are firstly estimated and an adaptive Asymptotically Uniformly Most Powerful
 (AUMP) test is designed (theoretically and practically), to check whether
 The former approach is based on a simplified parametric model of natural images.
 Parameters are firstly estimated and an adaptive Asymptotically Uniformly Most Powerful
 (AUMP) test is designed (theoretically and practically), to check whether


@@ -163,9 +163,9 @@ already noticed in the quality analysis presented in the previous section.
 Next, our approach is more easily detectable than HUGO, which
 is the most secure steganographic tool, as far as we know. 
 However by combining \emph{adaptive} and \emph{STC} strategies
 Next, our approach is more easily detectable than HUGO, which
 is the most secure steganographic tool, as far as we know. 
 However by combining \emph{adaptive} and \emph{STC} strategies

-our approach obtains similar results than HUGO ones.
+our approach obtains similar results to HUGO ones.

 
 However due to its 
 
 However due to its 

-huge number of features integration, it is not lightweight, which justifies 
+huge number of integration features, it is not lightweight, which justifies 

 in the authors' opinion the consideration of the proposed method.   
 
 in the authors' opinion the consideration of the proposed method.   
 

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,

diff --git a/main.tex b/main.tex
index a445067276789ff67f598efae175f505ef50fac9..f2c8449a93418dcc733af447b6f9d98a300f482e 100755 (executable)
--- a/main.tex
+++ b/main.tex
@@ -78,7 +78,7 @@ A novel steganographic method called STABYLO is introduced in
 this research work.
 Its main advantage is to be much lighter than the so-called
 Highly Undetectable steGO (HUGO) scheme, a well-known state of the art
 this research work.
 Its main advantage is to be much lighter than the so-called
 Highly Undetectable steGO (HUGO) scheme, a well-known state of the art

-steganographic process in spatial domain.
+steganographic process in the spatial domain.

 Additionally to this effectiveness, 
 quite comparable results through noise measures like PSNR-HVS-M, 
 and weighted PSNR (wPSNR) are obtained.
 Additionally to this effectiveness, 
 quite comparable results through noise measures like PSNR-HVS-M, 
 and weighted PSNR (wPSNR) are obtained.


@@ -117,7 +117,7 @@ pseudorandom number generator, together with Syndrome-Trellis Codes
 for minimizing distortion.
 After having introduced with details the proposed method,
 we have evaluated it through noise measures (namely, the PSNR, PSNR-HVS-M, 
 for minimizing distortion.
 After having introduced with details the proposed method,
 we have evaluated it through noise measures (namely, the PSNR, PSNR-HVS-M, 

-BIQI, and weighted PSNR) and using well-established steganalysers.
+BIQI, and weighted PSNR) and we have used well-established steganalysers.

 
 % Of course, other detectors like the fuzzy edge methods
 % deserve much further attention, which is why we intend 
 
 % Of course, other detectors like the fuzzy edge methods
 % deserve much further attention, which is why we intend 


@@ -138,7 +138,7 @@ examined for the sake of completeness. Finally, the
 systematic replacement of all the LSBs of edges by binary digits provided
 by the BBS generator will be investigated, and the consequences of such a 
 replacement, in terms of security, will be discussed.
 systematic replacement of all the LSBs of edges by binary digits provided
 by the BBS generator will be investigated, and the consequences of such a 
 replacement, in terms of security, will be discussed.

-Furthermore, we plan to investigate information hiding on other models, high frequency for JPEG encoding for instance.
+Furthermore, we plan to investigate information hiding on other models, such as high frequency for JPEG encoding.

 
 
 \bibliographystyle{plain}
 
 
 \bibliographystyle{plain}

diff --git a/ourapproach.tex b/ourapproach.tex
index e6f3d4ada250f0199618f6b2e27f24a999b70639..5389d35484badac9c346b0c8fc799c513c473de6 100644 (file)
--- a/ourapproach.tex
+++ b/ourapproach.tex
@@ -3,15 +3,15 @@ four main steps: the data encryption (Sect.~\ref{sub:bbs}),
 the cover pixel selection (Sect.~\ref{sub:edge}),
 the adaptive payload considerations (Sect.~\ref{sub:adaptive}),
 and how the distortion has been minimized (Sect.~\ref{sub:stc}).
 the cover pixel selection (Sect.~\ref{sub:edge}),
 the adaptive payload considerations (Sect.~\ref{sub:adaptive}),
 and how the distortion has been minimized (Sect.~\ref{sub:stc}).

-The message extraction is finally presented  (Sect.~\ref{sub:extract}) and a running example ends this section (Sect.~\ref{sub:xpl}). 
+The message extraction is then presented  (Sect.~\ref{sub:extract}) and a running example ends this section (Sect.~\ref{sub:xpl}). 

 
 
 The flowcharts given in Fig.~\ref{fig:sch}
 summarize our steganography scheme denoted by
 STABYLO, which stands for STeganography with cAnny, Bbs, binarY embedding at LOw cost.
 
 
 The flowcharts given in Fig.~\ref{fig:sch}
 summarize our steganography scheme denoted by
 STABYLO, which stands for STeganography with cAnny, Bbs, binarY embedding at LOw cost.

-What follows are successively details of the inner steps and flows inside 
-both the embedding stage (Fig.~\ref{fig:sch:emb}) 
-and the extraction one (Fig.~\ref{fig:sch:ext}).
+What follows are successively some details of the inner steps and the flows both inside 
+ the embedding stage (Fig.~\ref{fig:sch:emb}) 
+and inside the extraction one (Fig.~\ref{fig:sch:ext}).

 Let us first focus on the data embedding. 
 
 \begin{figure*}%[t]
 Let us first focus on the data embedding. 
 
 \begin{figure*}%[t]


@@ -48,7 +48,7 @@ Let us first focus on the data embedding.
 
 
 \subsection{Security considerations}\label{sub:bbs}
 
 
 \subsection{Security considerations}\label{sub:bbs}

-Among methods of message encryption/decryption 
+Among methods of the message encryption/decryption 

 (see~\cite{DBLP:journals/ejisec/FontaineG07} for a survey)
 we implement the Blum-Goldwasser cryptosystem~\cite{Blum:1985:EPP:19478.19501}
 that is based on the Blum Blum Shub~\cite{DBLP:conf/crypto/ShubBB82} 
 (see~\cite{DBLP:journals/ejisec/FontaineG07} for a survey)
 we implement the Blum-Goldwasser cryptosystem~\cite{Blum:1985:EPP:19478.19501}
 that is based on the Blum Blum Shub~\cite{DBLP:conf/crypto/ShubBB82} 


@@ -96,7 +96,7 @@ are searched in order to find edges.
 As far as fuzzy edge methods are concerned, they are obviously based on fuzzy logic to highlight edges.
 
 Canny filters, on their parts, are an old family of algorithms still remaining a state of the art edge detector. They can be well-approximated by first-order derivatives of Gaussians.
 As far as fuzzy edge methods are concerned, they are obviously based on fuzzy logic to highlight edges.
 
 Canny filters, on their parts, are an old family of algorithms still remaining a state of the art edge detector. They can be well-approximated by first-order derivatives of Gaussians.

-As the Canny algorithm is well known and studied, fast, and implementable
+As the Canny algorithm is fast, well known, has been studied in depth, and is implementable

 on many  kinds of architectures like FPGAs, smartphones,  desktop machines, and
 GPUs, we have chosen this edge detector for illustrative purpose.
 
 on many  kinds of architectures like FPGAs, smartphones,  desktop machines, and
 GPUs, we have chosen this edge detector for illustrative purpose.
 


@@ -154,7 +154,7 @@ is sufficient.
 Two methods may further be applied to select bits that 
 will be modified. 
 The first one randomly chooses the subset of pixels to modify by 
 Two methods may further be applied to select bits that 
 will be modified. 
 The first one randomly chooses the subset of pixels to modify by 

-applying the BBS PRNG again. This method is further denoted  as to \emph{sample}.
+applying the BBS PRNG again. This method is further denoted  as a \emph{sample}.

 Once this set is selected, a classical LSB replacement is applied to embed the 
 stego content.
 The second method is a direct application of the 
 Once this set is selected, a classical LSB replacement is applied to embed the 
 stego content.
 The second method is a direct application of the 


@@ -238,7 +238,7 @@ message is extracted.
 In this example, the cover image is  Lena, 
 which is a $512\times512$  image with 256 grayscale levels.
 The message is the poem Ulalume (E. A. Poe), which is constituted by 104 lines, 667
 In this example, the cover image is  Lena, 
 which is a $512\times512$  image with 256 grayscale levels.
 The message is the poem Ulalume (E. A. Poe), which is constituted by 104 lines, 667

-words, and 3754 characters, \textit{i.e.},  30032 bits.
+words, and 3,754 characters, \textit{i.e.},  30,032 bits.

 Lena and the first verses are given in Fig.~\ref{fig:lena}.
 
 \begin{figure}
 Lena and the first verses are given in Fig.~\ref{fig:lena}.
 
 \begin{figure}


@@ -267,7 +267,7 @@ $~$ In the ghoul-haunted woodland of Weir.
 \caption{Cover and message examples} \label{fig:lena}
 \end{figure}
 
 \caption{Cover and message examples} \label{fig:lena}
 \end{figure}
 

-The edge detection returns 18641 and 18455 pixels when $b$ is
+The edge detection returns 18,641 and 18,455 pixels when $b$ is

 respectively 7 and 6. These edges are represented in Figure~\ref{fig:edge}.
 
 
 respectively 7 and 6. These edges are represented in Figure~\ref{fig:edge}.
 
 


@@ -298,7 +298,7 @@ respectively 7 and 6. These edges are represented in Figure~\ref{fig:edge}.
 
 
 
 
 
 

-Only 9320 bits (resp. 9227 bits) are available for embedding 
+Only 9,320 bits (resp. 9,227 bits) are available for embedding 

 in the former configuration where $b$ is 7 (resp. where $b$ is 6).
 In both cases, about the third part of the poem is hidden into the cover.
 Results with \emph{adaptive+STC} strategy are presented in 
 in the former configuration where $b$ is 7 (resp. where $b$ is 6).
 In both cases, about the third part of the poem is hidden into the cover.
 Results with \emph{adaptive+STC} strategy are presented in