-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
+This research work takes place into the field of information hiding, considerably developed
+these last two decades. The proposed method for
+steganography considers digital images as covers, it belongs in the well investigated large category
+of spatial least significant bits (LSBs) replacement schemes.
+Let us recall that, in this LSBR 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,
+pixels with even values (resp. odd values) 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 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 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 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
+only if this one does not correspond to the secret bit.
+Since it is possible to make that probabilities of increasing or decreasing the pixel value are the same, for instance by considering well encrypted hidden messages, 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/2005,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}.
+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
modification minimizes a distortion function.
This distortion may be computed thanks to feature vectors that are embedded for instance in steganalysers
-given above.
+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 SPAM features (whose size is larger than $10^7$) to avoid overfitting a particular
+It takes into account so-called 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
+Thanks to this features 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
+and the 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 cover image, edges already break the continuity of pixels' intensity map and thus already present large variations with theirs neighboring
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.
+Our first 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}.
+Edge based steganographic schemes have been already studied, the most interesting
+approaches being detailed in~\cite{Luo:2010:EAI:1824719.1824720} and \cite{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
+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.
+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 distinguish error with LSB embedding is fewer than
+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 benefit of these 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.
+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
+One can notice that all the previously 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,
+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 signal.
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}
+cover signal.
+
+Additionally, in the steganographic context, the data hiding procedure is often required
+to be a reversible one. We thus need to be able to compute the same edge detection pixels set for the cover and the stego image. For this, we propose to apply the edge detection algorithm not on all the bits of the image, but to exclude the LSBs.
+Finally, even if the steganalysis discipline
+has done great leaps forward 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 message content.
+Doing so makes our steganographic protocol, in 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-treillis codes), and cryptography
+(Blum-Goldwasser encryption protocol) are combined
+to compute an efficient steganographic
+scheme, whose principal characteristics 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.
+Section~\ref{sec:experiments} shows experiments on image quality, steganalytic evaluation, complexity of our approach,
+and compares it to the state of the art steganographic schemes.
+Finally, concluding notes and future work are given in Section~\ref{sec:concl}.
