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[canny.git] / experiments.tex

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+For whole experiments, the whole set of 10000 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. 
+We restrict experiments to 
+this set of cover images since this paper is more focussed on 
+the methodology than benchmarking.    
+
+

 \subsection{Adaptive Embedding Rate} 
 
 \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, 
+the average size of the message that can be embedded is 16,445 bits.
+Its corresponds to an  average payload of 6.35\%. 
+
+
+
+
+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}
 The visual quality of the STABYLO scheme is evaluated in this section.
 
 \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 Peak Signal to Noise Ratio (PSNR), 

-the PSNR-HVS-M familly~\cite{PSECAL07,psnrhvsm11} , 
-the BIQI~\cite{MB10,biqi11} and 
-the weigthed PSNR (wPSNR)~\cite{DBLP:conf/ih/PereiraVMMP01}.
+the PSNR-HVS-M family~\cite{psnrhvsm11}, 
+the BIQI~\cite{MB10}, and 
+the weighted PSNR (wPSNR)~\cite{DBLP:conf/ih/PereiraVMMP01}.

 The first one is widely used but does not take into
 The first one is widely used but does not take into

-account Human Visual System (HVS).
-The other last ones have been designed to tackle this problem.
+account the Human Visual System (HVS).
+The other ones have been designed to tackle this problem.

 
 

-\begin{table}
+\begin{table*}

 \begin{center}
 \begin{center}

-\begin{tabular}{|c|c|c|}
+\begin{tabular}{|c|c|c||c|c|c|}

 \hline
 \hline

-Embedding rate &  Adaptive 
-10 \% &  \\
+Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}\\

 \hline
 \hline

-PSNR &  66.55    & 61.86     \\
+Embedding &   \multicolumn{2}{|c||}{Adaptive} & Fixed & \multicolumn{2}{|c|}{Fixed} \\

 \hline
 \hline

-PSNR-HVS-M & 78.6  & 72.9 \\
+Rate &   + STC &  + sample & 10\% & 10\%&6.35\%\\ 

 \hline
 \hline

-BIQI & 28.3 & 28.4 \\
+PSNR &  66.55 & 63.48  & 61.86  & 64.65 & 67.08  \\ 

 \hline
 \hline

-wPSNR & 86.43& 77.47 \\
+PSNR-HVS-M & 78.6 & 75.39 & 72.9 & 76.67 & 79.23 \\ 
+\hline
+BIQI & 28.3 & 28.28 & 28.4 & 28.28 & 28.28 \\ 
+\hline
+wPSNR & 86.43& 80.59 & 77.47& 83.03 & 87.8\\ 

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

-\caption{Quality measeures of our steganography approach\label{table:quality}} 
-\end{table}
-
-
-Compare to the Edge Adpative scheme detailed in~\cite{Luo:2010:EAI:1824719.1824720}, our both wPSNR and PSNR values are always higher than their ones.
+\caption{Quality Measures of Steganography Approaches\label{table:quality}} 
+\end{table*}
+
+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 really relevant.
+These results are not surprising since the adaptive strategy aims at 
+embedding messages whose length is decided according to an 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 equivalent
+results with the strategy 
+\emph{adaptive+STC} than HUGO with an average embedding rate set to 
+6.35\%. 
+This occurs with 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.
+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  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 rates from a large base of images. 
+Our approach outperforms the former thanks to the introduction of the STC 
+algorithm.

 
 

-\JFC{comparer aux autres approaches}

 
 
 
 
 
 


@@ -50,31 +121,41 @@ 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 
 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.
+machine learning step, which is often
+implemented as support vector machine,
+can be favorably executed thanks to an ensemble classifier.

 
 
 
 
 
 

-\begin{table}
+\begin{table*}

 \begin{center}
 \begin{center}

-\begin{tabular}{|c|c|c|c|}
-Shemes & \multicolumn{2}{|c|}{STABYLO} & HUGO\\
+%\begin{small}
+\begin{tabular}{|c|c|c|c|c|c|}

 \hline
 \hline

-Embedding rate &  Adaptive & 10 \% &  10 \%\\
+Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}\\

 \hline
 \hline

-AUMP & 0.39  & 0.22     &  0.50     \\
+Embedding &   \multicolumn{2}{|c|}{Adaptive} & Fixed & \multicolumn{2}{|c|}{Fixed} \\

 \hline
 \hline

-Ensemble Classifier &   &      &      \\
+Rate &   + STC &  + sample & 10\% & 10\%& 6.35\%\\ 
+\hline
+AUMP & 0.39  & 0.33  & 0.22     &  0.50 &  0.50  \\
+\hline
+Ensemble Classifier & 0.47 & 0.44 & 0.35     & 0.48 &  0.49  \\

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

+%\end{small}

 \end{center}
 \caption{Steganalysing STABYLO\label{table:steganalyse}} 
 \end{center}
 \caption{Steganalysing STABYLO\label{table:steganalyse}} 

-\end{table}
+\end{table*}
+

 
 

+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, which justifies 
+in the authors' opinion the consideration of the proposed method.