-This research work takes place in the field of information hiding, considerably developed
-these last two decades. The proposed method for
+This research work takes place in the field of information hiding, considerably developed for the 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 LSBR category, a subset of all the LSBs of the cover image is modified
+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,
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}.
+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
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, the probabilities of increasing or decreasing the alue 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 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
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.
+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 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.
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
+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 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}
-and Canny~\cite{Canny:1986:CAE:11274.11275} approaches. The goal of this combination
+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 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,
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 compares it to the HUGO's one.
+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}.
