From: cguyeux Date: Mon, 4 Feb 2013 09:53:49 +0000 (+0100) Subject: Abstract X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/commitdiff_plain/e556b479316abe0d4183c94c28a84f07f9f64c9c Abstract --- diff --git a/experiments.tex b/experiments.tex index c92062c..21d813d 100644 --- a/experiments.tex +++ b/experiments.tex @@ -16,7 +16,7 @@ The message length is thus defined to be the 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 as \emph{adaptive+STC}. -The second one randomly choose the subset of pixels to modify by +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. @@ -30,7 +30,7 @@ Its corresponds to an average payload of 6.35\%. In the latter, the embedding rate is defined as a percentage between the -number of the modified pixels and the length of the bit message. +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 a set of edge pixels with threshold that is decreasing until its cardinality @@ -44,12 +44,12 @@ Otherwise, pixels are again randomly chosen with BBS. 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{PSECAL07,psnrhvsm11} , +the PSNR-HVS-M family~\cite{PSECAL07,psnrhvsm11}, 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 the Human Visual System (HVS). -The other last ones have been designed to tackle this problem. +The other ones have been designed to tackle this problem. \begin{table} \begin{center} @@ -78,9 +78,9 @@ 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. +for the latter, except for the BIQI metrics where differences are not really relevant. These results are not surprising since the adaptive strategy aims at -embedding messages whose length is decided according to a higher threshold +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\%), @@ -96,7 +96,7 @@ 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} +Next, both the approaches~\cite{DBLP:journals/eswa/ChenCL10,Chang20101286} 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 @@ -119,9 +119,9 @@ Parameters are firstly estimated and an adaptive Asymptotically Uniformly Most P (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 favorably 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. @@ -148,5 +148,6 @@ Ensemble Classifier & 0.47 & 0.44 & 0.35 & 0.48 \\ 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. +huge number of features integration, it is not lightweight, which justifies +in authors' opinion the consideration of the proposed method. diff --git a/main.tex b/main.tex index 3da6ccc..fb10cbd 100755 --- a/main.tex +++ b/main.tex @@ -37,7 +37,14 @@ Steganography, least-significant-bit (LSB)-based steganography, edge detection, \end{IEEEkeywords} \begin{abstract} -Todo +A novel steganographic method called STABYLO is introduced in this research work. +Its main reason for being is to be much lighter than the so-called +Highly Undetectable steGO (HUGO) method, a well known state of the art +steganographic process, while presenting quite comparable results through +noise measures like PSNR-HVS-M, BIQI, and weighted PSNR (wPSNR). +To achieve this goal, famous experimented components of signal processing, +coding theory, and cryptography are combined together, leading to +a scheme that can reasonably face up-to-date steganalysers. \end{abstract} diff --git a/stc.tex b/stc.tex index e98589f..a544278 100644 --- a/stc.tex +++ b/stc.tex @@ -65,7 +65,7 @@ presented by Filler \emph{et al.} in~\cite{DBLP:conf/mediaforensics/FillerJF10} is a practical solution to this complexity. Thanks to this contribution, the solving algorithm has a linear complexity with respect to $n$. -First of all, Filler et al. compute the matrix $H$ +First of all, Filler \emph{et al.} compute the matrix $H$ by placing a small sub-matrix $\hat{H}$ of size $h × w$ next to each other and shifted down by one row. Thanks to this special form of $H$, one can represent