From: couchot Date: Thu, 20 Jun 2013 06:58:39 +0000 (+0200) Subject: exp X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/commitdiff_plain/3c3e9a12f31ac42d0c3e7eb1be25aeafb7f7b3db?hp=--cc exp --- 3c3e9a12f31ac42d0c3e7eb1be25aeafb7f7b3db diff --git a/experiments.tex b/experiments.tex index 2b1c9e3..aed0505 100644 --- a/experiments.tex +++ b/experiments.tex @@ -5,11 +5,16 @@ 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. +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}. +\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. @@ -48,65 +53,71 @@ The visual quality of the STABYLO scheme is evaluated in this section. For the sake of completeness, four metrics are computed in these experiments: the Peak Signal to Noise Ratio (PSNR), the PSNR-HVS-M family~\cite{psnrhvsm11}, -the BIQI~\cite{MB10}, and +%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 the Human Visual System (HVS). The other ones have been designed to tackle this problem. + + + \begin{table*} \begin{center} -\begin{tabular}{|c|c|c||c|c|c|} -\hline -Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}\\ +\begin{tabular}{|c|c|c||c|c|c|c|c|} \hline -Embedding & \multicolumn{2}{|c||}{Adaptive} & Fixed & \multicolumn{2}{|c|}{Fixed} \\ +Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR} \\ \hline -Rate & + STC & + sample & 10\% & 10\%&6.35\%\\ +Embedding & \multicolumn{2}{|c||}{Adaptive} & Fixed & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\ \hline -PSNR & 66.55 & 63.48 & 61.86 & 64.65 & 67.08 \\ +Rate & + STC & + sample & 10\% & 10\%&6.35\%& 10\%&6.35\%\\ \hline -PSNR-HVS-M & 78.6 & 75.39 & 72.9 & 76.67 & 79.23 \\ +PSNR & 66.55 (\textbf{-0.8\%}) & 63.48 & 61.86 & 64.65 & {67.08} & 60.8 & 62.9\\ \hline -BIQI & 28.3 & 28.28 & 28.4 & 28.28 & 28.28 \\ +PSNR-HVS-M & 78.6 (\textbf{-0.8\%}) & 75.39 & 72.9 & 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& 80.59 & 77.47& 83.03 & 87.8\\ +wPSNR & 86.43(\textbf{-1.6\%}) & 80.59 & 77.47& 83.03 & {87.8} & & 80.6\\ \hline \end{tabular} \end{center} -\caption{Quality Measures of Steganography Approaches\label{table:quality}} +\caption{Quality Measures of Steganography Approaches\label{table:quality}} +\label{table:quality} \end{table*} -Let us give an interpretation of these experiments. + + +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, except for the BIQI metrics where differences are not really relevant. +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 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. +However our appraoch always outperforms EAISLSBMR since this one may modify +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. +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 -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 +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. -Our approach outperforms the former thanks to the introduction of the STC -algorithm. @@ -123,28 +134,29 @@ 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 an adaptive Asymptotically Uniformly Most Powerful (AUMP) test is designed (theoretically and practically), to check whether -an image has stego content or not. +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 favorably executed thanks to an ensemble classifier. - +%citer le second tableau, comparer avec EAISLSBMR \begin{table*} \begin{center} %\begin{small} \begin{tabular}{|c|c|c|c|c|c|c|c|} \hline -Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAIS}\\ +Schemes & \multicolumn{3}{|c|}{STABYLO} & \multicolumn{2}{|c|}{HUGO}& \multicolumn{2}{|c|}{EAISLSBMR}\\ \hline Embedding & \multicolumn{2}{|c|}{Adaptive} & Fixed & \multicolumn{2}{|c|}{Fixed}& \multicolumn{2}{|c|}{Fixed} \\ \hline Rate & + STC & + sample & 10\% & 10\%& 6.35\%& 10\%& 6.35\%\\ \hline -AUMP & 0.39 & 0.33 & 0.22 & 0.50 & 0.50 & & \\ +AUMP & 0.39 & 0.33 & 0.22 & 0.50 & 0.50 & 0.49 & 0.50 \\ \hline -Ensemble Classifier & \textbf{0.47} & 0.44 & 0.35 & 0.48 & 0.49 & 0.22 & 0.46 \\ +Ensemble Classifier & \textbf{0.47} & 0.44 & 0.35 & 0.48 & 0.49 & 0.43 & 0.46 \\ \hline \end{tabular} diff --git a/intro.tex b/intro.tex index db092e2..a8433e5 100644 --- a/intro.tex +++ b/intro.tex @@ -67,9 +67,14 @@ in a cover image, edges already break the continuity of pixels' intensity map a pixels. In other words, minor changes in regular areas are more dramatic than larger modifications in edge ones. Our first proposal is thus to embed message bits into edge shapes while preserving other smooth regions. -Edge based steganographic schemes have already been studied, the most interesting -approaches being detailed in~\cite{Luo:2010:EAI:1824719.1824720} and \cite{DBLP:journals/eswa/ChenCL10}. -In the former, the authors show how to select sharper edge regions with respect +Edge based steganographic schemes have already been studied, +the most interesting +approaches being detailed in~\cite{Luo:2010:EAI:1824719.1824720} and +in~\cite{DBLP:journals/eswa/ChenCL10}. +In the former, the authors presents the Edge Adaptive +Image Steganography based on lsb matching revisited further denoted as to +EAISLSBMR. This approach selects sharper edge + regions with respect to a given embedding rate: the larger the number of bits to be embedded, the coarser the edge regions are. Then the data hiding algorithm is achieved by applying LSBMR on pixels of these regions.