-This research work takes place in the field of information hiding, considerably developed for the last two decades. The proposed method for
+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.
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}.
+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 corrected considering the LSB matching (LSBM) subcategory, in which
-the $+1$ or $-1$ is randomly added to the cover pixel LSB value
+can be fixed by considering the LSB matching (LSBM) subcategory, in which
+a $+1$ 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 alue of pixels are equal. Then usual statistical approaches
+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.
-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 the steganalysers
+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} is one of the most efficient instance of such a scheme.
-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 the differences between
+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.
-Thanks to this features set, HUGO allows to embed messages that are $7\times$ longer than the former ones with the same level of
+The features embedded in WOW and UNIWARD are based on Wavelet-based
+directional filter. Thus, similarly, 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.
+However, this improvement has a larger computation cost, mainly due to
+ the distortion function
+computation.
There remains a large place between random selection of LSB and feature based modification of pixel values.
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 achieved by applying LSBMR on some of the pixels of these regions.
+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
-thanks to extensive experiments.
+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 advantage of these optimized embeddings, provided they are not too time consuming.
+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}
+The Edge detection is computed by combining fuz\-zy 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 of the data hiding scheme.
One can notice that all the previously referenced
-schemes~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10,DBLP:conf/ih/PevnyFB10}
+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.
-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.
+the better the approach is said to be.
+For instance, studied payloads range from 0.04 to 0.4 modified bits per pixel.
+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 detected.
+Practically speaking, if Alice would send
+a hidden message to Bob, she would never consider
+such kind of image and a high embedding rate.
+The approach we propose here is thus to provide a small complexity
+self adaptive algorithm
+with an acceptable payload, which
+depends on the cover signal.
+The payload is further said to
+ be acceptable if it allows to embed a sufficiently
+long message in the cover signal.
+Practically speaking, our approach is efficient enough for
+payloads close to 0.06 bit per pixel which allows to embed
+messages of length larger than 15,728 bits in an
+image of size $512\times 512$ pixels.
+
% 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,
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
+To sum up, 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
+(Blum-Goldwas\-ser encryption protocol) are combined in this research work.
+The objective is to compute an efficient steganographic
+sche\-me, 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 the HUGO's one.
+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}.
-
-
