-This work considers digital images as covers and it is based on
-spatial least significant-bit (LSB) replacement.
-In this data hiding scheme, a subset of all the LSBs of the cover image is modified
-with a secret bit stream depending on 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 thus it may break the
-structural symmetry of the host image.
-Such structural alterations can be detected by
+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}.
-This drawback is avoided in LSB matching (LSBM) where
+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 the pixel value are the same, statistical approaches
-cannot be applied here to discover stego-images in LSBM.
+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 thanks to extracted features from neighboring elements of noise residual.
+which classify images according to extracted features from neighboring elements of residual noise.
-LSB matching revisited (LSBMR) has been recently introduced in~\cite{Mielikainen06}.
-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.
-\CG{Je pige pas cette phrase, et je comprends pas l'explication du LSBMR :/}
-
-
-In such a way, the modification
+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 in the cover image at the same payload compared to
-LSB replacement and LSBM. It is also shown in~\cite{Mielikainen06} 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 in the
LSBM approach. % based on our experiments
This distortion may be computed thanks to feature vectors that are embedded for instance in 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 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
