Relecture

[canny.git] / experiments.tex

diff --git a/experiments.tex b/experiments.tex
index a2126d3e108ef36dc1c25bb359cc93df75ccc3f1..c92062c01027a8f4eed07bcf6fcf9b4e835d9192 100644 (file)
--- a/experiments.tex
+++ b/experiments.tex
@@ -1,4 +1,4 @@
-For the whole experiment, a set of 500 images is randomly extracted 
+For whole experiments, a set of 500 images is randomly extracted 

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


@@ -6,13 +6,12 @@ grayscale digital image.
 
 \subsection{Adaptive Embedding Rate} 
 
 
 \subsection{Adaptive Embedding Rate} 
 

-Two strategies have been developed in our scheme with respect to the rate of 
-embedding which is either \emph{adaptive} or \emph{fixed}.
+Two strategies have been developed in our scheme, depending on the embedding rate that is either \emph{adaptive} or \emph{fixed}.

 
 In the former the embedding rate depends on the number of edge pixels.
 
 In the former the embedding rate depends on the number of edge pixels.

-The higher it is, the larger is the message length that can be considered.
+The higher it is, the larger is the message length that can be inserted.

 Practically, a set of edge pixels is computed according to the 
 Practically, a set of edge pixels is computed according to the 

-Canny algorithm with high threshold.
+Canny algorithm with an high threshold.

 The message length is thus defined to be the half of this set cardinality.
 In this strategy, two methods are thus applied to extract bits that 
 are modified. The first one is a direct application of the STC algorithm.
 The message length is thus defined to be the half of this set cardinality.
 In this strategy, two methods are thus applied to extract bits that 
 are modified. The first one is a direct application of the STC algorithm.


@@ -20,10 +19,9 @@ This method is further referred as \emph{adaptive+STC}.
 The second one randomly choose the subset of pixels to modify by 
 applying the BBS PRNG again. This method is denoted \emph{adaptive+sample}.
 Notice that the rate between 
 The second one randomly choose the subset of pixels to modify by 
 applying the BBS PRNG again. This method is denoted \emph{adaptive+sample}.
 Notice that the rate between 

-available bits  and bit message length is always equal to two.
+available bits  and bit message length is always equal to 2.

 This constraint is indeed induced by the fact that the efficiency 
 of the STC algorithm is unsatisfactory under that threshold.
 This constraint is indeed induced by the fact that the efficiency 
 of the STC algorithm is unsatisfactory under that threshold.

-

 On our experiments and with the adaptive scheme, 
 the average size of the message that can be embedded is 16445.
 Its corresponds to an  average payload of 6.35\%. 
 On our experiments and with the adaptive scheme, 
 the average size of the message that can be embedded is 16445.
 Its corresponds to an  average payload of 6.35\%. 


@@ -37,20 +35,20 @@ This is the classical approach adopted in steganography.
 Practically, the Canny algorithm generates a 
 a set of edge pixels with threshold that is decreasing until its cardinality
 is sufficient. If the set cardinality is more than twice larger than the 
 Practically, the Canny algorithm generates a 
 a set of edge pixels with threshold that is decreasing until its cardinality
 is sufficient. If the set cardinality is more than twice larger than the 

-bit message length an STC step is again applied.
+bit message length, a STC step is again applied.

 Otherwise, pixels are again randomly chosen with BBS.
 
  
 
 \subsection{Image Quality}
 The visual quality of the STABYLO scheme is evaluated in this section.
 Otherwise, pixels are again randomly chosen with BBS.
 
  
 
 \subsection{Image Quality}
 The visual quality of the STABYLO scheme is evaluated in this section.

-Four metrics are computed in these experiments: 
+For the sake of completeness, four metrics are computed in these experiments: 

 the Peak Signal to Noise Ratio (PSNR), 
 the PSNR-HVS-M family~\cite{PSECAL07,psnrhvsm11} , 
 the Peak Signal to Noise Ratio (PSNR), 
 the PSNR-HVS-M family~\cite{PSECAL07,psnrhvsm11} , 

-the BIQI~\cite{MB10,biqi11} and 
+the BIQI~\cite{MB10,biqi11}, and 

 the weighted PSNR (wPSNR)~\cite{DBLP:conf/ih/PereiraVMMP01}.
 The first one is widely used but does not take into
 the weighted PSNR (wPSNR)~\cite{DBLP:conf/ih/PereiraVMMP01}.
 The first one is widely used but does not take into

-account Human Visual System (HVS).
+account the Human Visual System (HVS).

 The other last ones have been designed to tackle this problem.
 
 \begin{table}
 The other last ones have been designed to tackle this problem.
 
 \begin{table}


@@ -85,10 +83,10 @@ These results are not surprising since the adaptive strategy aims at
 embedding messages whose length is decided according to a higher threshold
 into the edge detection.  
 Let us focus on the quality of HUGO images: with a given fixed 
 embedding messages whose length is decided according to a higher threshold
 into the edge detection.  
 Let us focus on the quality of HUGO images: with a given fixed 

-embedding rate (10\%) 
+embedding rate (10\%), 

 HUGO always produces images whose quality is higher than the STABYLO's one.
 However, our approach nevertheless provides better results with the strategy 
 HUGO always produces images whose quality is higher than the STABYLO's one.
 However, our approach nevertheless provides better results with the strategy 

-adaptive+STC in a lightweight manner, as motivated in the introduction.      
+\emph{adaptive+STC} in a lightweight manner, as motivated in the introduction.      

 
 
 Let us now compare the STABYLO approach with other edge based steganography
 
 
 Let us now compare the STABYLO approach with other edge based steganography


@@ -96,7 +94,7 @@ schemes with respect to the image quality.
 First of all, the Edge Adaptive
 scheme detailed in~\cite{Luo:2010:EAI:1824719.1824720} 
 executed with a 10\% embedding rate 
 First of all, the Edge Adaptive
 scheme detailed in~\cite{Luo:2010:EAI:1824719.1824720} 
 executed with a 10\% embedding rate 

-has the same PSNR but a lower wPSNR than our:
+has the same PSNR but a lower wPSNR than ours:

 these two metrics are respectively equal to 61.9 and 68.9. 
 Next both the approaches~\cite{DBLP:journals/eswa/ChenCL10,Chang20101286}
 focus on increasing the payload while the PSNR is acceptable, but do not 
 these two metrics are respectively equal to 61.9 and 68.9. 
 Next both the approaches~\cite{DBLP:journals/eswa/ChenCL10,Chang20101286}
 focus on increasing the payload while the PSNR is acceptable, but do not 


@@ -117,13 +115,13 @@ and Ensemble Classifier~\cite{DBLP:journals/tifs/KodovskyFH12} based steganalyse
 Both aims at detecting hidden bits in grayscale natural images and are 
 considered as the state of the art of steganalysers in spatial domain~\cite{FK12}.
 The former approach is based on a simplified parametric model of natural images.
 Both aims at detecting hidden bits in grayscale natural images and are 
 considered as the state of the art of steganalysers in spatial domain~\cite{FK12}.
 The former approach is based on a simplified parametric model of natural images.

-Parameters are firstly estimated and a adaptive Asymptotically Uniformly Most Powerful
-(AUMP) test is designed (theoretically and practically) to check whether
-a natural image has stego content or not.  
+Parameters are firstly estimated and an adaptive Asymptotically Uniformly Most Powerful
+(AUMP) test is designed (theoretically and practically), to check whether
+an image has stego content or not.  

 In the latter, the authors show that the 
 machine learning step, (which is often
 implemented as support vector machine)
 In the latter, the authors show that the 
 machine learning step, (which is often
 implemented as support vector machine)

-can be a favourably executed thanks to an Ensemble Classifiers.
+can be favorably executed thanks to an Ensemble Classifiers.

 
 
 
 
 
 


@@ -137,9 +135,9 @@ Embedding &   \multicolumn{2}{|c|}{Adaptive} & Fixed & Fixed \\
 \hline
 Rate &   + STC &  + sample & 10\% & 10\%\\ 
 \hline
 \hline
 Rate &   + STC &  + sample & 10\% & 10\%\\ 
 \hline

-AUMP & 0.39  & & 0.22     &  0.50     \\
+AUMP & 0.39  & 0.33  & 0.22     &  0.50     \\

 \hline
 \hline

-Ensemble Classifier & 0.47 &  & 0.35     & 0.48     \\
+Ensemble Classifier & 0.47 & 0.44 & 0.35     & 0.48     \\

 
 \hline
 \end{tabular}
 
 \hline
 \end{tabular}


@@ -148,7 +146,7 @@ Ensemble Classifier & 0.47 &  & 0.35     & 0.48     \\
 \end{table}
 
 
 \end{table}
 
 

-Results show that our approach is more easily detectable than HUGO which is
-is the more secure steganography tool, as far we know. However due to its 
+Results show that our approach is more easily detectable than HUGO, which
+is the most secure steganographic tool, as far as we know. However due to its 

 huge number of features integration, it is not lightweight.   
 
 huge number of features integration, it is not lightweight.