X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/b335b39d3ad21e598f9a7172a3e95ffe0d48a736..03f2db2f776786ee6e9435d3c0f603b248cdd2ae:/experiments.tex diff --git a/experiments.tex b/experiments.tex index 11af844..8194a53 100644 --- a/experiments.tex +++ b/experiments.tex @@ -1,33 +1,217 @@ +For all the 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 on benchmarks. -\subsection{Image Quality} +We use the matrices $\hat{H}$ +generated by the integers given +in Table~\ref{table:matrices:H} +as introduced in~\cite{FillerJF11}, since these ones have experimentally +be proven to have the best modification efficiency. +For instance if the rate between the size of the message and the size of the +cover vector +is 1/4, each number in $\{81, 95, 107, 121\}$ is translated into a binary number +and each one constitutes thus a column of $\hat{H}$. + +\begin{table} +$$ +\begin{array}{|l|l|} +\hline +\textrm{Rate} & \textrm{Matrix generators} \\ +\hline +{1}/{2} & \{71,109\}\\ +\hline +{1}/{3} & \{95, 101, 121\}\\ +\hline +{1}/{4} & \{81, 95, 107, 121\}\\ +\hline +{1}/{5} & \{75, 95, 97, 105, 117\}\\ +\hline +{1}/{6} & \{73, 83, 95, 103, 109, 123\}\\ +\hline +{1}/{7} & \{69, 77, 93, 107, 111, 115, 121\}\\ +\hline +{1}/{8} & \{69, 79, 81, 89, 93, 99, 107, 119\}\\ +\hline +{1}/{9} & \{69, 79, 81, 89, 93, 99, 107, 119, 125\}\\ +\hline +\end{array} +$$ +\caption{Matrix Generator for $\hat{H}$ in STC}\label{table:matrices:H} +\end{table} + + +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. +It 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}\label{sub:quality} The visual quality of the STABYLO scheme is evaluated in this section. -Three 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~\cite{PSECAL07,psnrhvsm11} and the BIQI~\cite{MB10,biqi11}. +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 -account Human Visual System (HVS). -The two 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. + +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|c|} +\hline +Schemes & \multicolumn{4}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR} \\ +\hline +Embedding & Fixed & \multicolumn{3}{|c|}{Adaptive (about 6.35\%)} & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\ +\hline +Rate & 10\% & + sample & +STC(7) & +STC(6) & 10\%&6.35\%& 10\%&6.35\%\\ +\hline +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\%}) & 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\%})& 86.28 & 83.03 & {87.8} & 76.7 & 80.6\\ +\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} +\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 last two 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 in a lightweight manner. + + +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}\label{sub:steg} + + +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 steganalyser. +This approach aims at detecting hidden bits in grayscale natural +images and is +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. +% 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. +% , the authors show that the +% machine learning step, which is often +% implemented as a support vector machine, +% can be favorably executed thanks to an ensemble classifier. -\subsection{Steganalysis} +\begin{table*} +\begin{center} +%\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 +Embedding & Fixed & \multicolumn{3}{|c|}{Adaptive (about 6.35\%)} & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\ +\hline +Rate & 10\% & + sample & +STC(7) & +STC(6) & 10\%& 6.35\%& 10\%& 6.35\%\\ +\hline +%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} +%\end{small} +\end{center} +\caption{Steganalysing STABYLO\label{table:steganalyse}} +\end{table*} -The 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. -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. -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. +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. +%%%%et pour b= 6 ? -\JFC{Raphael, il faut donner des résultats ici} \ No newline at end of file +Compared to EAILSBMR, we obtain better results when the strategy is +\emph{adaptive}. +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.