Additionally to (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 the steganalysers
+This distortion may be computed thanks to feature vectors that
+ are embedded for instance in the steganalysers
referenced above.
-The 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 over-fitting a particular
-steganalyser. Thus a distortion measure for each pixel is individually determined as the sum of the differences between
+The Highly Undetectable steGO (HUGO) method~\cite{DBLP:conf/ih/PevnyFB10},
+WOW~\cite{conf/wifs/HolubF12}, and UNIWARD~\cite{HFD14}
+are some of the most efficient instances of such a scheme.
+
+HUGO takes into account so-called SPAM features.
+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.
-Due to this features set, HUGO allows to embed messages that are $7$ times longer than the former ones with the same level of
+The features embedded in WOW and UNIWARD are based on Wavelet-based
+directional filter. Thus, similarly, the distortion function is
+the sum of the differences between these wavelet coefficients
+computed from the cover and from the stego images.
+
+
+Due to this distortion measures, HUGO, WOW and UNIWARD allow
+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.
+However, this improvement has a larger computation cost, mainly due to
+ the distortion function
+calculus.
There remains a large place between random selection of LSB and feature based modification of pixel values.
sche\-mes~\cite{Luo:2010:EAI:1824719.1824720,DBLP:journals/eswa/ChenCL10,DBLP:conf/ih/PevnyFB10}
produce stego contents
by only considering the payload, not the type of image signal: the higher the payload is,
-the better the approach is said to be.
-Contrarily, we argue that some images should not be taken as a cover because of the nature of their signals.
-Consider for instance a uniformly black image: a very tiny modification of its pixels can be easily detectable.
-The approach we propose is thus to provide a self adaptive algorithm with a high payload, which depends on the cover signal.
+the better the approach is said to be.
+For instance, studied payloads range from 0.04 to 0.4 modified bits per pixel.
+Contrarily, we argue that some images should not be taken
+as a cover because of the nature of their signals.
+Consider for instance a uniformly black image: a very tiny modification of its
+pixels can be easily detectable.
+Practically speaking, if Alice would send
+a hidden message to Bob, she would never consider
+such kind of image and a high embedding rate.
+The approach we propose here is thus to provide a small complexity
+self adaptive algorithm
+with an acceptable payload, which
+depends on the cover signal.
+The payload is further said to
+ be acceptable if it allows to embed a sufficiently
+long message in the cover signal.
+Practically speaking, our approach is efficient enough for
+payloads close to 0.06 bit per pixel which allows to embed
+messages of length larger than 16000 bits in an
+image of size $512\times 512$ pixels.
+
% Message extraction is achieved by computing the same
% edge detection pixels set for the cover and the stego image.
% The edge detection algorithm is thus not applied on all the bits of the image,
Section~\ref{sec:ourapproach} presents the details of the proposed steganographic scheme and applies it on a running example. Among its technical description,
its adaptive aspect is emphasized.
Section~\ref{sub:complexity} presents the overall complexity of our approach
-and compares it to the HUGO's one.
+and compares it to HUGO, WOW, and UNIWARD.
Section~\ref{sec:experiments} shows experiments on image quality, steganalysis evaluation, and compares them to the state of the art steganographic schemes.
Finally, concluding notes and future work are given in Section~\ref{sec:concl}.
