X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/36f33066d192d54e79b234d89f223b3f4016ff12..165a29713fcc3fb0e7bdbf25129fc1e33d14667c:/experiments.tex?ds=sidebyside diff --git a/experiments.tex b/experiments.tex index 45f0b87..c92062c 100644 --- a/experiments.tex +++ b/experiments.tex @@ -1,15 +1,32 @@ +For whole experiments, a set of 500 images is randomly extracted +from the database taken from the BOSS contest~\cite{Boss10}. +In this set, each cover is a $512\times 512$ +grayscale digital image. + + \subsection{Adaptive Embedding Rate} -Two strategies have been developed in our scheme with respect to the rate of -embedding which is either \emph{ adaptive} or \emph{fixed}. +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 is the message length that can be considered. +The higher it is, the larger is the message length that can be inserted. Practically, a set of edge pixels is computed according to the -Canny algorithm with high threshold. +Canny algorithm with an high threshold. The message length is thus defined to be the half of this set cardinality. -The rate between available bits and bit message length is then more than two.This constraint is indeed induced by the fact that the efficiency -of the stc algorithm is unsatisfactory under that threshold. +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 as \emph{adaptive+STC}. +The second one randomly choose 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. +On our experiments and with the adaptive scheme, +the average size of the message that can be embedded is 16445. +Its corresponds to an average payload of 6.35\%. + + In the latter, the embedding rate is defined as a percentage between the @@ -18,55 +35,76 @@ This is the classical approach adopted in steganography. Practically, the Canny algorithm generates a a set of edge pixels with threshold that is decreasing until its cardinality is sufficient. If the set cardinality is more than twice larger than the -bit message length an stc is again applied. -Otherwise, pixels are randomly chosen from the set of pixels to build the -subset with a given size. The BBS PRNG is again applied there. - +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 family~\cite{PSECAL07,psnrhvsm11} , -the BIQI~\cite{MB10,biqi11} and +the BIQI~\cite{MB10,biqi11}, 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). +account the Human Visual System (HVS). The other last ones have been designed to tackle this problem. \begin{table} \begin{center} -\begin{tabular}{|c|c|c|} +\begin{tabular}{|c|c|c||c|c|} +\hline +Schemes & \multicolumn{3}{|c|}{STABYLO} & HUGO\\ \hline -Embedding rate & Adaptive & 10 \% \\ +Embedding & \multicolumn{2}{|c||}{Adaptive} & Fixed & Fixed \\ \hline -PSNR & 66.55 & 61.86 \\ +Rate & + STC & + sample & 10\% & 10\%\\ \hline -PSNR-HVS-M & 78.6 & 72.9 \\ +PSNR & 66.55 & 63.48 & 61.86 & 64.65 \\ \hline -BIQI & 28.3 & 28.4 \\ +PSNR-HVS-M & 78.6 & 75.39 & 72.9 & 76.67\\ \hline -wPSNR & 86.43& 77.47 \\ +BIQI & 28.3 & 28.28 & 28.4 & 28.28\\ +\hline +wPSNR & 86.43& 80.59 & 77.47& 83.03\\ \hline \end{tabular} \end{center} -\caption{Quality measures of our steganography approach\label{table:quality}} +\caption{Quality Measures of Steganography Approaches\label{table:quality}} \end{table} - -Let us compare the STABYLO approach with other edge based steganography +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 relevant. +These results are not surprising since the adaptive strategy aims at +embedding messages whose length is decided according to a 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 better results with the strategy +\emph{adaptive+STC} in 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. -Fist off all, wPSNR and PSNR of the Edge Adaptive -scheme detailed in~\cite{Luo:2010:EAI:1824719.1824720} are lower than ours. +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 the approaches~\cite{DBLP:journals/eswa/ChenCL10,Chang20101286} -focus on increasing the payload while the PSNR is acceptable, bu do not +focus on increasing the payload while the PSNR is acceptable, but do not give quality metrics for fixed embedding rate from a large base of images. -Our approach outperforms the former thanks to the introduction of the stc +Our approach outperforms the former thanks to the introduction of the STC algorithm. + + \subsection{Steganalysis} @@ -77,27 +115,29 @@ 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. -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 machine learning step, (which is often implemented as support vector machine) -can be a favourably executed thanks to an Ensemble Classifiers. +can be favorably executed thanks to an Ensemble Classifiers. \begin{table} \begin{center} -\begin{tabular}{|c|c|c|c|} +\begin{tabular}{|c|c|c|c|c|} +\hline +Schemes & \multicolumn{3}{|c|}{STABYLO} & HUGO\\ \hline -Schemes & \multicolumn{2}{|c|}{STABYLO} & HUGO\\ +Embedding & \multicolumn{2}{|c|}{Adaptive} & Fixed & Fixed \\ \hline -Embedding rate & Adaptive & 10 \% & 10 \%\\ +Rate & + STC & + sample & 10\% & 10\%\\ \hline -AUMP & 0.39 & 0.22 & 0.50 \\ +AUMP & 0.39 & 0.33 & 0.22 & 0.50 \\ \hline -Ensemble Classifier & & & \\ +Ensemble Classifier & 0.47 & 0.44 & 0.35 & 0.48 \\ \hline \end{tabular} @@ -106,7 +146,7 @@ Ensemble Classifier & & & \\ \end{table} -Results show that our approach is more easily detectable than HUGO which is -is the more secure steganography tool, as far we know. However due to its +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.