X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/fdf9d8855582d89072dcd1339af9f5b74a167ac6..ef06623aa40e69d9e5332208f9ead5af2e7ea4b6:/intro.tex?ds=inline diff --git a/intro.tex b/intro.tex index 745751f..28463ee 100644 --- a/intro.tex +++ b/intro.tex @@ -1,110 +1,146 @@ - -This work considers digital images as covers and foundation is -spatial least significant-bit (LSB) replacement. -In 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. -For a given pair of pixels, in which the LSB -of the first pixel carries one bit of secret message, and the relationship -(odd–even combination) of the two pixel values carries -another bit of secret message. - - - -In such a way, the modification +This research work takes place in the field of information hiding, considerably developed these last two decades. The proposed method for +steganography considers digital images as covers. +It belongs to the well-known large category +of spatial least significant bits (LSBs) replacement schemes. +Let us recall that, in this LSB replacement category, a subset of all the LSBs of the cover image is modified +with a secret bit stream depending on: a secret key, the cover, and the message to embed. +In this well-studied steganographic approach, +if we consider that a LSB is the last bit of each pixel value, +pixels with an even value (resp. an odd value) +are never decreased (resp. increased), +thus such schemes may break the +structural symmetry of the host images. +And these structural alterations can be detected by +well-designed statistical investigations, leading to well-known +steganalysis methods~\cite{DBLP:journals/tsp/DumitrescuWW03,DBLP:conf/mmsec/FridrichGD01,Dumitrescu:2005:LSB:1073170.1073176}. + +Let us recall too that this drawback +can be fixed by considering the LSB matching (LSBM) subcategory, in which +the $+1$ or $-1$ is randomly added to the cover pixel's LSB value +only if this one does not correspond to the secret bit. +%TODO : modifier ceci +By considering well-encrypted hidden messages, the probabilities of increasing or decreasing the 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}, +which classify images according to extracted features from neighboring elements of residual noise. + + +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 +a second bit of the message. +By doing so, the modification rate of pixels can decrease from 0.5 to 0.375 bits/pixel (bpp) in the case of a maximum embedding rate, meaning fewer -changes to the cover image at the same payload compared to -LSB replacement and LSBM. It is also shown that such a new +changes in the cover image at the same payload compared to both +LSBR and LSBM. It is also shown in~\cite{Mielikainen06} that such a new scheme can avoid the LSB replacement style asymmetry, and -thus it should make the detection slightly more difficult than the +thus it should make the detection slightly more difficult than in the LSBM approach. % based on our experiments -Instead of (efficiently) modifying LSBs, there is also a need to select pixels whose value +Additionally to (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 +This distortion may be computed thanks to feature vectors that + are embedded for instance in the steganalysers +referenced above. +The Highly Undetectable steGO (HUGO) method~\cite{DBLP:conf/ih/PevnyFB10}, +WOW~\cite{conf/wifs/HolubF12}, and UNIWARD~\cite{HFD14} +are some of the most efficient instances of such a scheme. + +HUGO takes into account so-called SPAM features. +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. +The features embedded in WOW and UNIWARD are based on Wavelet-based +directional filter. Thus, similarelly, the distortion function is +the sum of the differences between these wavelet coefficients +computed from the cover and from the stego images. + + +Due to this distortion measures, HUGO, WOW and UNIWARD allow +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. 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 are 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 been 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 +Edges form the outline of an object: they are the boundaries between overlapping objects or between an object +and its background. When producing the stego-image, a small modification of some pixel values in such edges should not impact the image quality, which is a requirement when +attempting to be undetectable. Indeed, +in a cover image, edges already break the continuity of pixels' intensity map and thus already present large variations with their neighboring +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 +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 +EAISLSBMR. This approach selects sharper edge + regions with respect +to a given embedding rate: the larger the number of bits to be embedded is, the coarser +the edge regions are. +Then the data hiding algorithm is achie\-ved 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 +through 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 embedding, provided it is not too time consuming. -In the latter, an hybrid edge detector is presented followed by an ad'hoc -embedding approach. +We thus propose to take advantage of this optimized embedding, 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} -and Canny~\cite{Canny:1986:CAE:11274.11275} approaches. The goal of this combination -is to enlarge the set of modified bits to increase the payload. +and Canny~\cite{Canny:1986:CAE:11274.11275} approaches. +The goal of this combination +is to enlarge the set of modified bits to increase the payload of the data hiding scheme. -But, one can notice that all the previous referenced -schemes~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10,DBLP:conf/ih/PevnyFB10} -produce stego content -with only considering the payload, not the type of image signal: the higher the payload is, +One can notice that all the previously referenced +sche\-mes~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10,DBLP:conf/ih/PevnyFB10} +produce stego contents +by only considering the payload, not the type of image signal: the higher the payload is, the better the approach is said to be. -Contrarely, we argue that some images should not be taken as a cover because of the nature of their signal. +Contrarily, we argue that some images should not be taken as a cover because of the nature of their signals. Consider for instance a uniformly black image: a very tiny modification of its pixels can be easily detectable. -The approach we propose is thus to provide a self adaptive algorithm with a high payload, which depends on the -cover signal. - - - -\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, which takes into consideration the cover image -an which 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} - - +The approach we propose is thus to provide a self adaptive algorithm with a high payload, which depends on the cover signal. +% Message extraction is achieved by computing the same +% edge detection pixels set for the cover and the stego image. +% The edge detection algorithm is thus not applied on all the bits of the image, +% but to exclude the LSBs which are modified. + +Finally, even if the steganalysis discipline + 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, +even in the worst case scenario, the attacker +will not be able to obtain the original message content. +Doing so makes our steganographic protocol, to a certain extend, an asymmetric one. + +To sum up, in this research work, well-studied and experimented +techniques of signal processing (adaptive edges detection), +coding theory (syndrome-trellis codes), and cryptography +(Blum-Goldwasser encryption protocol) are combined +to compute an efficient steganographic +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. Among its technical description, +its adaptive aspect is emphasized. +Section~\ref{sub:complexity} presents the overall complexity of our approach +and compares it to HUGO, WOW, and UNIWARD. +Section~\ref{sec:experiments} shows experiments on image quality, steganalysis evaluation, and compares them to the state of the art steganographic schemes. +Finally, concluding notes and future work are given in Section~\ref{sec:concl}.