complexity

[canny.git] / experiments.tex

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-\subsection{Adaptive Embedding Rate} 
+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. 
+We restrict experiments to 
+this set of cover images since this paper is more focused on 
+the methodology than benchmarking.    
+We use the matrices given in table~\ref{table:matrices:H}
+as introduced in~\cite{}, since these ones have experimentally 
+be proven to have the best modification efficiency.
+
+\begin{table}
+$$
+\begin{array}{|l|l|}
+\textrm{rate} & \textrm{matrix generators} \\
+$\frac{1}{2} & \{71,109\}
+$\frac{1}{3} & \{95, 101, 121\}
+$\frac{1}{4} & \{81, 95, 107, 121\}
+$\frac{1}{5} & \{75, 95, 97, 105, 117\}
+$\frac{1}{6} & \{73, 83, 95, 103, 109, 123\}
+$\frac{1}{7} & \{69, 77, 93, 107, 111, 115, 121\}
+$\frac{1}{8} & \{69, 79, 81, 89, 93, 99, 107, 119\}
+$\frac{1}{9} & \{69, 79, 81, 89, 93, 99, 107, 119, 125]
+
+
+
+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 latter is the work that is the closest to ours, as far as we know. 
+
+
+
+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. 
+Sections~\ref{sub:quality} and~\ref{sub:steg} respectively present 
+the quality analysis and the security of our scheme. 

 
 
 
 
 
 

-\subsection{Image Quality}
+ 
+
+\subsection{Image quality}\label{sub:quality}

 The visual quality of the STABYLO scheme is evaluated in this section.
 The visual quality of the STABYLO scheme is evaluated in this section.

-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 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}
+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{center}

-\begin{tabular}{|c|c|c|}
+\begin{tabular}{|c|c|c||c|c|c|c|c|c|}
+\hline
+Schemes & \multicolumn{4}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR} \\

 \hline
 \hline

-Embedding rate &  Adaptive 
-10 \% &  \\
+Embedding &   Fixed & \multicolumn{3}{|c|}{Adaptive (about 6.35\%)} &  \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\

 \hline
 \hline

-PSNR &  66.55    & 61.86     \\
+Rate &   10\% &  + sample &  +STC(7) & +STC(6) &  10\%&6.35\%& 10\%&6.35\%\\ 

 \hline
 \hline

-PSNR-HVS-M & 78.6  & 72.9 \\
+PSNR & 61.86 & 63.48 &  66.55 (\textbf{-0.8\%}) &  63.7  & 64.65 & {67.08} & 60.8 & 62.9\\ 

 \hline
 \hline

-BIQI & 28.3 & 28.4 \\
+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
 \hline

-wPSNR & 86.43& 77.47 \\
+wPSNR & 77.47 & 80.59 & 86.43(\textbf{-1.6\%})& 86.28  & 83.03 & {87.8} & 76.7 & 80.6\\ 

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

+
+\begin{footnotesize}
+\vspace{2em}
+Variances given in bold font express the quality differences between 
+HUGO and STABYLO with  STC+adaptive parameters.
+\end{footnotesize}
+

 \end{center}
 \end{center}

-\caption{Quality measeures of our steganography approach\label{table:quality}} 
-\end{table}
+\caption{Quality measures of steganography approaches\label{table:quality}}
+\end{table*}
+
+
+
+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 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 
+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 is always better than EAISLSBMR since this one may modify 
+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\%)
+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. 
+
+

 
 

+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:
+providing an efficient steganography approach with a lightweight manner.

 
 

-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.

 
 

-\JFC{comparer aux autres approaches}
+Let us now compare the STABYLO approach with other edge based steganography
+approaches, namely~\cite{DBLP:journals/eswa/ChenCL10,Chang20101286}.
+These two schemes 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. 

 
 
 
 
 
 

-\subsection{Steganalysis}

 
 

+\subsection{Steganalysis}\label{sub:steg}

 
 
 
 

-The quality of our approach has been evaluated through the two 
+
+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.
 AUMP~\cite{Fillatre:2012:ASL:2333143.2333587}
 and Ensemble Classifier~\cite{DBLP:journals/tifs/KodovskyFH12} based steganalysers.

-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}.
+Both aim at detecting hidden bits in grayscale natural images and are 
+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.
 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.
+This approach is dedicated to verify whether LSB has been modified 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 a 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|c|c|c|}
+\hline
+Schemes & \multicolumn{4}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR}\\

 \hline
 \hline

-Embedding rate &  Adaptive & 10 \% &  10 \%\\
+Embedding & Fixed &   \multicolumn{3}{|c|}{Adaptive (about 6.35\%)}  & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\

 \hline
 \hline

-AUMP & 0.39  & 0.22     &  0.50     \\
+Rate & 10\% &  + sample &   +STC(7) & +STC(6)   & 10\%& 6.35\%& 10\%& 6.35\%\\ 

 \hline
 \hline

-Ensemble Classifier &   &      &      \\
+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 & 0.47     & 0.48 &  0.49  &  0.43  & 0.46 \\

 
 \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 are summarized in Table~\ref{table:steganalyse}.
+First of all, STC outperforms the sample strategy for the two steganalysers, as 
+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
+our approach obtains similar results to HUGO ones.

 
 

+However due to its 
+huge number of integration features, it is not lightweight, which justifies 
+in the authors' opinion the consideration of the proposed method.