-This research work takes place into the field of information hiding, considerably developed
+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 in the well investigated large category
+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
with a secret bit stream depending on: a secret key, the cover, and the message to embed.
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.
-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
+%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
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 according to extracted features from neighboring elements of residual noise.
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
-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.
+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.
However, this improvement is time consuming, mainly due to the distortion function
There remains a large place between random selection of LSB and feature based modification of pixel values.
We argue that modifying edge pixels is an acceptable compromise.
-Edges form the outline of an object: they are the boundary between overlapping objects or between an object
-and the background. When producing the stego-image, a small modification of some pixel values in such edges should not impact the image quality, which is a requirement when
+Edges form the outline of an object: they are the boundaries between overlapping objects or between an object
+and its background. When producing the stego-image, a small modification of some pixel values in such edges should not impact the image quality, which is a requirement when
attempting to be undetectable. Indeed,
-in cover image, edges already break the continuity of pixels' intensity map and thus already present large variations with theirs neighboring
-pixels. In other words, minor changes in regular area are more dramatic than larger modifications in edge ones.
+in a cover image, edges already break the continuity of pixels' intensity map and thus already present large variations with their neighboring
+pixels. In other words, minor changes in regular areas are more dramatic than larger modifications in edge ones.
Our first proposal is thus to embed message bits into edge shapes while preserving other smooth regions.
-Edge based steganographic schemes have been already studied, the most interesting
+Edge based steganographic schemes have already been studied, the most interesting
approaches being detailed in~\cite{Luo:2010:EAI:1824719.1824720} and \cite{DBLP:journals/eswa/ChenCL10}.
In the former, the authors show how to select sharper edge regions with respect
to a given embedding rate: the larger the number of bits to be embedded, the coarser
cover signal.
Additionally, in the steganographic context, the data hiding procedure is often required
-to be a reversible one. We thus need to be able to compute the same edge detection pixels set for the cover and the stego image. For this, we propose to apply the edge detection algorithm not on all the bits of the image, but to exclude the LSBs.
-Finally, even if the steganalysis discipline
-has done great leaps forward these last years, it is currently impossible to prove rigorously
+to be a reversible one. We thus need to be able to compute the same edge detection pixels set for the cover and the stego image. For this, we propose to apply the edge detection algorithm not on all the bits of the image, 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
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,
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.
+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
-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
+scheme, 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 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}.
