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.
It takes into account so-called SPAM features
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
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}
% 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,
