X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/25660891acf240f523434aed3481b240c32f1ad1..457be557d1bb33d5d169605464822243e0ed6232:/intro.tex?ds=inline diff --git a/intro.tex b/intro.tex index ccdad75..cb4c669 100644 --- a/intro.tex +++ b/intro.tex @@ -19,13 +19,13 @@ can be corrected considering the LSB matching (LSBM) subcategory, in which the $+1$ or $-1$ is randomly added to the cover pixel LSB value only if this one does not correspond to the secret bit. %TODO : modifier ceci -Since it is possible to make that probabilities of increasing or decreasing the pixel value, for instance by considering well-encrypted hidden messages, usual statistical approaches +By considering well-encrypted hidden messages, probabilities of increasing or of decreasing value of pixels are equal. Then usual statistical approaches cannot be applied here to discover stego-contents in LSBM. -The most accurate detectors for this matching are universal steganalysers such as~\cite{LHS08,DBLP:conf/ih/Ker05,FK12}, +The most accurate detectors for this matching are universal steganalysers such as~\cite{LS08,DBLP:conf/ih/Ker05,FK12}, which classify images according to extracted features from neighboring elements of residual noise. -Finally, LSB matching revisited (LSBMR) has been recently introduced in~\cite{Mielikainen06}. +Finally, LSB matching revisited (LSBMR) has recently been introduced in~\cite{Mielikainen06}. It works as follows: for a given pair of pixels, the LSB of the first pixel carries a first bit of the secret message, while the parity relationship (odd/even combination) of the two pixel values carries @@ -45,7 +45,7 @@ LSBM approach. % based on our experiments Instead of (efficiently) modifying LSBs, there is also a need to select pixels whose value modification minimizes a distortion function. -This distortion may be computed thanks to feature vectors that are embedded for instance in steganalysers +This distortion may be computed thanks to feature vectors that are embedded for instance in the steganalysers referenced above. Highly Undetectable steGO (HUGO) method~\cite{DBLP:conf/ih/PevnyFB10} is one of the most efficient instance of such a scheme. It takes into account so-called SPAM features @@ -53,8 +53,8 @@ It takes into account so-called SPAM features to avoid overfitting a particular steganalyser. Thus a distortion measure for each pixel is individually determined as the sum of the differences between the features of the SPAM computed from the cover and from the stego images. -Thanks to this features set, HUGO allows to embed $7\times$ longer messages with the same level of -indetectability than LSB matching. +Thanks to this features set, HUGO allows to embed messages that are $7\times$ longer than the former ones with the same level of +indetectability as LSB matching. However, this improvement is time consuming, mainly due to the distortion function computation. @@ -73,17 +73,17 @@ 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 present the Edge Adaptive -Image Steganography based on LSB matching revisited further denoted as to +Image Steganography based on LSB matching revisited further denoted as 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. +Then the data hiding algorithm is achieved by applying LSBMR on some of the pixels of these regions. The authors show that their proposed method is more efficient than all the LSB, LSBM, and LSBMR approaches thanks to extensive experiments. However, it has been shown that the distinguishing error with LSB embedding is lower than the one with some binary embedding~\cite{DBLP:journals/tifs/FillerJF11}. -We thus propose to take benefit of these optimized embeddings, provided they are not too time consuming. +We thus propose to take advantage of these optimized embeddings, provided they are not too time consuming. In the latter, an hybrid edge detector is presented followed by an ad hoc embedding. The Edge detection is computed by combining fuzzy logic~\cite{Tyan1993} @@ -105,7 +105,7 @@ The approach we propose is thus to provide a self adaptive algorithm with a high % but to exclude the LSBs which are modified. Finally, even if the steganalysis discipline - has done great leaps forward these last years, it is currently impossible to prove rigorously + has known great innovations these last years, it is currently impossible to prove rigorously that a given hidden message cannot be recovered by an attacker. This is why we add to our scheme a reasonable message encryption stage, to be certain that, @@ -122,9 +122,11 @@ scheme, whose principal characteristic is to take into consideration the cover image and to be compatible with small computation resources. The remainder of this document is organized as follows. -Section~\ref{sec:ourapproach} presents the details of the proposed steganographic scheme and applies it on a running example. -Section~\ref{sec:experiments} shows experiments on image quality, steganalytic evaluation, complexity of our approach, -and compare them to the state of the art steganographic schemes. +Section~\ref{sec:ourapproach} presents the details of the proposed steganographic scheme and applies it on a running example. Among its technical description, +its adaptive aspect is emphasized. +Section~\ref{sub:complexity} presents the overall complexity of our approach +and compare it to the HUGO's one. +Section~\ref{sec:experiments} shows experiments on image quality, steganalysis evaluation, and compare them to the state of the art steganographic schemes. Finally, concluding notes and future work are given in Section~\ref{sec:concl}.