X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/f4086cd758c530e5056b03d442f1cb5cf07b447b..93faa138ef3fae7589a5dc3ba0d8862f7b54c4cb:/experiments.tex

diff --git a/experiments.tex b/experiments.tex
index 0c08d69..9c73025 100644
--- a/experiments.tex
+++ b/experiments.tex
@@ -7,50 +7,25 @@ this set of cover images since this paper is more focused on
 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 less detectable information hiding tool in spatial domain 
+and the later is the work which is close to ours, as far as we know. 
 
 
 
-
-
-\subsection{Adaptive Embedding Rate} 
-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 the message length that can be inserted is.
-Practically, a set of edge pixels is computed according to the 
-Canny algorithm with an high threshold.
-The message length is thus defined to be 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.
-This method is further referred to as \emph{adaptive+STC}.
-The second one randomly chooses 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.
-In our experiments and with the adaptive scheme, 
+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 Sections~\ref{sub:quality} and~\ref{sub:steg} respectively present 
+the quality analysis and the security of our scheme. 
 
 
 
-
-In the latter, the embedding rate is defined as a percentage between the 
-number of modified pixels and the length of the bit message.
-This is the classical approach adopted in steganography.
-Practically, the Canny algorithm generates  
-a set of edge pixels related to a threshold that is decreasing until its cardinality
-is sufficient. If the set cardinality is more than twice larger than the 
-bit message length, a STC step is again applied.
-Otherwise, pixels are again randomly chosen with BBS.
-
  
 
-\subsection{Image Quality}
+\subsection{Image Quality}\label{sub:quality}
 The visual quality of the STABYLO scheme is evaluated in this section.
-For the sake of completeness, four metrics are computed in these experiments: 
+For the sake of completeness, three metrics are computed in these experiments: 
 the Peak Signal to Noise Ratio (PSNR), 
 the PSNR-HVS-M family~\cite{psnrhvsm11}, 
 %the BIQI~\cite{MB10}, 
@@ -60,26 +35,32 @@ The first one is widely used but does not take into
 account the Human Visual System (HVS).
 The other ones have been designed to tackle this problem.
 
+If we apply them on the running example, 
+the PSNR, PSNR-HVS-M, and wPSNR values are respectively equal to 
+68.39, 79.85, and 89.71 for the stego Lena when $b$ is equal to 7.
+If $b$ is 6, these values are respectively equal to 
+65.43, 77.2, and 89.35.
+
 
 
 
 \begin{table*}
 \begin{center}
-\begin{tabular}{|c|c|c||c|c|c|c|c|}
+\begin{tabular}{|c|c|c||c|c|c|c|c|c|}
 \hline
-Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR} \\
+Schemes & \multicolumn{4}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR} \\
 \hline
-Embedding &   Fixed & \multicolumn{2}{|c|}{Adaptive} &  \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
+Embedding &   Fixed & \multicolumn{3}{|c|}{Adaptive} &  \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
 \hline
-Rate &   10\% &  + sample & + STC  &  10\%&6.35\%& 10\%&6.35\%\\ 
+Rate &   10\% &  + sample &  +STC(7) & +STC(6) &  10\%&6.35\%& 10\%&6.35\%\\ 
 \hline
-PSNR & 61.86 & 63.48 &  66.55 (\textbf{-0.8\%})     & 64.65 & {67.08} & 60.8 & 62.9\\ 
+PSNR & 61.86 & 63.48 &  66.55 (\textbf{-0.8\%}) &  63.7  & 64.65 & {67.08} & 60.8 & 62.9\\ 
 \hline
-PSNR-HVS-M & 72.9 & 75.39 & 78.6 (\textbf{-0.8\%})    & 76.67 & {79.23} & 61.3  & 63.4\\ 
+PSNR-HVS-M & 72.9 & 75.39 & 78.6 (\textbf{-0.8\%}) & 75.5  & 76.67 & {79.23} & 71.8  & 74.3\\ 
 %\hline
 %BIQI & 28.3 & 28.28 & 28.4 & 28.28 & 28.28 & 28.2 & 28.2\\ 
 \hline
-wPSNR & 77.47 & 80.59 & 86.43(\textbf{-1.6\%})  & 83.03 & {87.8} & 76.7 & 80.6\\ 
+wPSNR & 77.47 & 80.59 & 86.43(\textbf{-1.6\%})& 86.28  & 83.03 & {87.8} & 76.7 & 80.6\\ 
 \hline
 \end{tabular}
 
@@ -96,7 +77,7 @@ HUGO and STABYLO with  STC+adaptive parameters.
 
 
 Results are summarized into the Table~\ref{table:quality}.
-Let us give an interpretation of these first experiments.
+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 
@@ -113,6 +94,11 @@ the two least significant bits whereas STABYLO only alter LSB.
 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.
+In this column STC(7) stands for embeding data in the LSB whereas
+in STC(6), data are hidden in the two last significant bits. 
+
+
+
 The quality variance between HUGO and STABYLO for these parameters
 is given in bold font. It is always close to 1\% which confirms 
 the objective presented in the motivations:
@@ -128,7 +114,7 @@ give quality metrics for fixed embedding rates from a large base of images.
 
 
 
-\subsection{Steganalysis}
+\subsection{Steganalysis}\label{sub:steg}
 
 
 
@@ -151,17 +137,17 @@ can be favorably executed thanks to an ensemble classifier.
 \begin{table*}
 \begin{center}
 %\begin{small}
-\begin{tabular}{|c|c|c|c|c|c|c|c|}
+\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
 \hline
-Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR}\\
+Schemes & \multicolumn{4}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR}\\
 \hline
-Embedding & Fixed &   \multicolumn{2}{|c|}{Adaptive}  & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
+Embedding & Fixed &   \multicolumn{3}{|c|}{Adaptive}  & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\
 \hline
-Rate & 10\% &  + sample &   + STC   & 10\%& 6.35\%& 10\%& 6.35\%\\ 
+Rate & 10\% &  + sample &   +STC(7) & +STC(6)   & 10\%& 6.35\%& 10\%& 6.35\%\\ 
 \hline
-AUMP & 0.22 & 0.33 & 0.39         &  0.50 &  0.50 & 0.49 & 0.50 \\
+AUMP & 0.22 & 0.33 & 0.39  &   0.45    &  0.50 &  0.50 & 0.49 & 0.50 \\
 \hline
-Ensemble Classifier & 0.35 & 0.44 & 0.47       & 0.48 &  0.49  &  0.43  & 0.46 \\
+Ensemble Classifier & 0.35 & 0.44 & 0.47 & 0.47     & 0.48 &  0.49  &  0.43  & 0.46 \\
 
 \hline
 \end{tabular}
@@ -178,6 +164,7 @@ 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.
+
 However due to its 
 huge number of features integration, it is not lightweight, which justifies 
 in the authors' opinion the consideration of the proposed method.