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
+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.
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 so-called 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 features set, HUGO allows to embed $7\times$ longer messages with the same level of
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 an asymetric one.
+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 treatment (adaptive edge detection),
-coding theory (binary embedding), and cryptography
+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 our steganographic scheme.
-Section~\ref{sec:experiments} shows experiments on image quality, steganalytic evaluation, complexity of our approach
-and compares it to state of the art steganographic schemes.
+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}.
