X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/e1408e22528148d27bf1cc1edc56764345c64cba..963b0c034c8a7c7f1fb77fc6b06e3ba6744c1dbd:/experiments.tex?ds=inline

diff --git a/experiments.tex b/experiments.tex
index cd2ed1a..c92062c 100644
--- a/experiments.tex
+++ b/experiments.tex
@@ -1,15 +1,32 @@
+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.
+
+
 \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.
-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 
-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.
-The rate between  available bits  and bit message length is then more than two.This constraint is indeed induced by the fact that the efficiency 
+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.
+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 
+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.
+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\%. 
+
+
 
 
 In the latter, the embedding rate is defined as a percentage between the 
@@ -18,48 +35,67 @@ 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 
-bit message length an STC step is again applied.
-Otherwise, pixels are randomly chosen from the set of pixels to build the 
-subset with a given size. The BBS PRNG is again applied there.
- 
+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.
-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 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
-account Human Visual System (HVS).
+account the Human Visual System (HVS).
 The other last ones have been designed to tackle this problem.
 
 \begin{table}
 \begin{center}
-\begin{tabular}{|c|c|c|}
+\begin{tabular}{|c|c|c||c|c|}
+\hline
+Schemes & \multicolumn{3}{|c|}{STABYLO} & HUGO\\
 \hline
-Embedding rate &  Adaptive & 10 \%  \\
+Embedding &   \multicolumn{2}{|c||}{Adaptive} & Fixed & Fixed \\
 \hline
-PSNR &  66.55    & 61.86     \\
+Rate &   + STC &  + sample & 10\% & 10\%\\ 
 \hline
-PSNR-HVS-M & 78.6  & 72.9 \\
+PSNR &  66.55 & 63.48  & 61.86  & 64.65   \\ 
 \hline
-BIQI & 28.3 & 28.4 \\
+PSNR-HVS-M & 78.6 & 75.39 & 72.9 & 76.67\\ 
 \hline
-wPSNR & 86.43& 77.47 \\
+BIQI & 28.3 & 28.28 & 28.4 & 28.28\\ 
+\hline
+wPSNR & 86.43& 80.59 & 77.47& 83.03\\ 
 \hline
 \end{tabular}
 \end{center}
-\caption{Quality measures of our steganography approach\label{table:quality}} 
+\caption{Quality Measures of Steganography Approaches\label{table:quality}} 
 \end{table}
 
-
-Let us compare the STABYLO approach with other edge based steganography
+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.
+Numerical results are indeed always greater for the former strategy than 
+for the latter, except for the BIQI metrics where differences are not relevant.
+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 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 
+\emph{adaptive+STC} in a lightweight manner, as motivated in the introduction.      
+
+
+Let us now compare the STABYLO approach with other edge based steganography
 schemes with respect to the image quality.
-Fist off all, wPSNR and PSNR of the Edge Adaptive
-scheme detailed in~\cite{Luo:2010:EAI:1824719.1824720} are lower than ours.
+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 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 
 give quality metrics for fixed embedding rate from a large base of images. 
@@ -67,6 +103,8 @@ Our approach outperforms the former thanks to the introduction of the STC
 algorithm.
 
 
+
+
 \subsection{Steganalysis}
 
 
@@ -77,27 +115,29 @@ 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.
-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)
-can be a favourably executed thanks to an Ensemble Classiï¬ers.
+can be favorably executed thanks to an Ensemble Classiï¬ers.
 
 
 
 \begin{table}
 \begin{center}
-\begin{tabular}{|c|c|c|c|}
+\begin{tabular}{|c|c|c|c|c|}
+\hline
+Schemes & \multicolumn{3}{|c|}{STABYLO} & HUGO\\
 \hline
-Schemes & \multicolumn{2}{|c|}{STABYLO} & HUGO\\
+Embedding &   \multicolumn{2}{|c|}{Adaptive} & Fixed & Fixed \\
 \hline
-Embedding rate &  Adaptive & 10 \% &  10 \%\\
+Rate &   + STC &  + sample & 10\% & 10\%\\ 
 \hline
-AUMP & 0.39  & 0.22     &  0.50     \\
+AUMP & 0.39  & 0.33  & 0.22     &  0.50     \\
 \hline
-Ensemble Classifier & 0.47   & 0.35     & 0.48     \\
+Ensemble Classifier & 0.47 & 0.44 & 0.35     & 0.48     \\
 
 \hline
 \end{tabular}
@@ -106,7 +146,7 @@ Ensemble Classifier & 0.47   & 0.35     & 0.48     \\
 \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.   
 
