X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/24da70a2907152b45f175222de4962951669ac12..b417a74f270da1ad1a8b11552a8508c45cb75085:/experiments.tex diff --git a/experiments.tex b/experiments.tex index b952fae..0c08d69 100644 --- a/experiments.tex +++ b/experiments.tex @@ -1,39 +1,130 @@ +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 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}. + + + + + +\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. -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 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 -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{tabular}{|c|c|c|} +\begin{tabular}{|c|c|c||c|c|c|c|c|} +\hline +Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR} \\ \hline -Embedding rate & Adaptive -10 \% & \\ +Embedding & Fixed & \multicolumn{2}{|c|}{Adaptive} & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\ \hline -PSNR & & \\ +Rate & 10\% & + sample & + STC & 10\%&6.35\%& 10\%&6.35\%\\ \hline -PSNR-HVS-M & 78.6 & 72.9 \\ +PSNR & 61.86 & 63.48 & 66.55 (\textbf{-0.8\%}) & 64.65 & {67.08} & 60.8 & 62.9\\ \hline -BIQI & 28.3 & 28.4 \\ +PSNR-HVS-M & 72.9 & 75.39 & 78.6 (\textbf{-0.8\%}) & 76.67 & {79.23} & 61.3 & 63.4\\ +%\hline +%BIQI & 28.3 & 28.28 & 28.4 & 28.28 & 28.28 & 28.2 & 28.2\\ \hline -wPSNR & 86.43& 77.47 \\ +wPSNR & 77.47 & 80.59 & 86.43(\textbf{-1.6\%}) & 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 measeures of our steganography approach\label{table:quality}} -\end{table} +\caption{Quality Measures of Steganography Approaches\label{table:quality}} +\end{table*} + + +Results are summarized into the Table~\ref{table:quality}. +Let us give an interpretation of these first 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. +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 always outperforms EAISLSBMR since this one may modify +the two least significant bits whereas STABYLO only alter LSB. -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. +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. +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. + + +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. -\JFC{comparer aux autres approaches} @@ -41,37 +132,53 @@ Compare to the Edge Adpative scheme detailed in~\cite{Luo:2010:EAI:1824719.18247 -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. 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. +This approach is dedicated to verify whether LSB has been modified 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. - +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{tabular}{|c|c|c|c|} -Shemes & \multicolumn{2}{|c|}{STABYLO} & HUGO\\ +%\begin{small} +\begin{tabular}{|c|c|c|c|c|c|c|c|} \hline -Embedding rate & Adaptive & 10 \% & 10 \%\\ +Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR}\\ \hline -AUMP & 0.39 & 0.22 & 0.50 \\ +Embedding & Fixed & \multicolumn{2}{|c|}{Adaptive} & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\ \hline -Ensemble Classifier & & & \\ +Rate & 10\% & + sample & + STC & 10\%& 6.35\%& 10\%& 6.35\%\\ +\hline +AUMP & 0.22 & 0.33 & 0.39 & 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 \\ \hline \end{tabular} +%\end{small} \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 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.