From: couchot Date: Fri, 1 Jun 2012 13:43:05 +0000 (+0200) Subject: analyse -> intro X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/commitdiff_plain/4215ba2b6bbe5b20d0a4f33c2922ec5b85d5c03e?ds=inline;hp=-c analyse -> intro --- 4215ba2b6bbe5b20d0a4f33c2922ec5b85d5c03e diff --git a/intro.tex b/intro.tex new file mode 100644 index 0000000..f8974ce --- /dev/null +++ b/intro.tex @@ -0,0 +1,75 @@ + +This work considers digital images as covers and foundation is +spatial least significant-bit (LSB) replacement. +I this data hiding scheme a subset of all the LSB of the cover image is modified +with a secret bit stream depending on to a key, the cover, and the message to embed. +This well studied steganographic approach never decreases (resp. increases) +pixel with even value (resp. odd value) and may break structural symmetry. +These structural modification can be detected by statistical approaches +and thus by steganalysis methods~\cite{DBLP:journals/tsp/DumitrescuWW03,DBLP:conf/mmsec/FridrichGD01,Dumitrescu:2005:LSB:1073170.1073176} + +This drawback is avoided in LSB matching (LSBM) where +the $+1$ or $-1$ is randomly added to the cover pixel LSB value +only if this one does not match the secret bit. +Since probabilities of increasing or decreasing pixel value are the same, statistical approaches +cannot be applied there to discover stego-images in LSBM. +The most accurate detectors for this matching are universal steganalysers such as~\cite{LHS08,DBLP:conf/ih/2005,FK12} +which classify images thanks to extracted features from neighboring elements of noise residual. + +LSB matching revisited (LSBMR)~\cite{Mielikainen06} have been recently introduced. +This scheme deals with pairs of pixels instead of individual ones. +It thus allows to decrease the number of modified bits per cover pixel +for the same payload compared to LSB replacement and LSBM and +and avoids the LSB replacement style asymmetry. Unfortunately, +detectors referenced above are able to distinguish between +stego content images and cover images. + +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 +given 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 SPAM features (whose size is larger than $10^7$) to avoid overfitting a particular +steganalyser. Thus a distortion measure for each pixel is individually determined as the sum of differences between +features of SPAM computed from the cover and from the stego images. +Thanks to this feature set, HUGO allows to embed $7\times$ longer messages with the same level of +indetectability than LSB matching. +However, this improvement is time consuming, mainly due to the distortion function +computation. + +There remains a large place between random selection of LSB and feature based modification of pixel values. +We argue that modifying edge pixels is an acceptable compromise. +Edges form the outline of an object: they are the boundary between overlapping objects or between an object +and the background. A small modification of pixel value in the stego image should not be harmful to the image quality: +in cover image, edge pixels already break its continuity and thus already contains large variation with neighbouring +pixels. In other words, minor changes in regular area is more dramatic than larger modifications in edge ones. +Our proposal is thus to embed message bits into edge shapes while preserving other smooth regions. + +Edge based steganographic schemes have bee already studied~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10}. +In the former, the authors show how to select sharper edge regions with respect +to embedding rate: the larger the number of bits to be embedded, the coarse the edge regions are. +Then the data hiding algorithm is achieved by applying LSBMR on pixels of this region. +The authors show that this method is more efficient than all the LSB, LSBM, LSBMR approaches +thanks to extensive experiments. +However, it has been shown that the distinguish error with LSB embedding is fewer than the one with some binary embedding~\cite{DBLP:journals/tifs/FillerJF11}. +We thus propose to take benefit of these optimized embedding, provided it is not too time consuming. +Experiments have confirmed such a fact\JFC{Raphael....}. + + +\JFC{Christophe : énoncer la problématique du besoin de crypto et de ``cryptographiquement sûr'', les algo déjà cassés.... +l'efficacité d'un encodage/décodage ...} +To deal with security issues, message is encrypted + +In this paper, we thus propose to combine tried and tested techniques of signal theory (the adaptive edge detection), coding (the binary embedding), and cryptography +(the encrypt the message) to compute an efficient steganography scheme that is amenable to be executed on small devices. + +The rest of the paper is organised as follows. +Section~\ref{sec:ourapproach} presents the details of our steganographic scheme. +Section~\ref{sec:experiments} shows experiments on image quality, steganalytic evaluation, complexity of our approach +and compare them to state of the art steganographic schemes. +Finally, concluding notes and future works are given in section~\ref{sec:concl} + +theory : ? + + +