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.
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.
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.
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.
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.
+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
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.
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
-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.
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
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
message encryption stage, to be certain that,
even in the worst case scenario, the attacker
will not be able to obtain the message content.
message encryption stage, to be certain that,
even in the worst case scenario, the attacker
will not be able to obtain the message content.
To sum up, in this research work, well studied and experimented
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
To sum up, in this research work, well studied and experimented
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
consideration the cover image and to be compatible with small computation resources.
The remainder of this document is organized as follows.
consideration the cover image and to be compatible with small computation resources.
The remainder of this document is organized as follows.
