-For whole experiments, the whole set of 10000 images
+For whole experiments, the whole set of 10,000 images
of the BOSS contest~\cite{Boss10} database is taken.
In this set, each cover is a $512\times 512$
grayscale digital image in a RAW format.
Our approach is always compared to Hugo~\cite{DBLP:conf/ih/PevnyFB10}
and to EAISLSBMR~\cite{Luo:2010:EAI:1824719.1824720}.
The former is the least detectable information hiding tool in spatial domain
-and the later is the work that is close to ours, as far as we know.
+and the latter is the work that is the closest to ours, as far as we know.
the average size of the message that can be embedded is 16,445 bits.
Its corresponds to an average payload of 6.35\%.
The two other tools will then be compared with this payload.
-The Sections~\ref{sub:quality} and~\ref{sub:steg} respectively present
+Sections~\ref{sub:quality} and~\ref{sub:steg} respectively present
the quality analysis and the security of our scheme.
Results are summarized in Table~\ref{table:quality}.
Let us give an interpretation of these experiments.
First of all, the adaptive strategy produces images with lower distortion
-than the one of images resulting from the 10\% fixed strategy.
+than the images resulting from the 10\% fixed strategy.
Numerical results are indeed always greater for the former strategy than
for the latter one.
These results are not surprising since the adaptive strategy aims at
If we combine \emph{adaptive} and \emph{STC} strategies
(which leads to an average embedding rate equal to 6.35\%)
-our approach provides equivalent metrics than HUGO.
+our approach provides metrics equivalent to those provided by HUGO.
In this column STC(7) stands for embedding data in the LSB whereas
in STC(6), data are hidden in the two last significant bits.
AUMP~\cite{Fillatre:2012:ASL:2333143.2333587}
and Ensemble Classifier~\cite{DBLP:journals/tifs/KodovskyFH12} based steganalysers.
Both aim at detecting hidden bits in grayscale natural images and are
-considered as the state of the art of steganalysers in spatial domain~\cite{FK12}.
+considered as state of the art steganalysers in the spatial domain~\cite{FK12}.
The former approach is based on a simplified parametric model of natural images.
Parameters are firstly estimated and an adaptive Asymptotically Uniformly Most Powerful
(AUMP) test is designed (theoretically and practically), to check whether
Next, our approach is more easily detectable than HUGO, which
is the most secure steganographic tool, as far as we know.
However by combining \emph{adaptive} and \emph{STC} strategies
-our approach obtains similar results than HUGO ones.
+our approach obtains similar results to HUGO ones.
However due to its
-huge number of features integration, it is not lightweight, which justifies
+huge number of integration features, it is not lightweight, which justifies
in the authors' opinion the consideration of the proposed method.
which classify images according to extracted features from neighboring elements of residual noise.
-Finally, LSB matching revisited (LSBMR) has been recently introduced in~\cite{Mielikainen06}.
+Finally, LSB matching revisited (LSBMR) has recently been 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
Instead of (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 steganalysers
+This distortion may be computed thanks to feature vectors that are embedded for instance in the 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
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.
+Thanks to this features set, HUGO allows 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.
approaches being detailed in~\cite{Luo:2010:EAI:1824719.1824720} and
in~\cite{DBLP:journals/eswa/ChenCL10}.
In the former, the authors present the Edge Adaptive
-Image Steganography based on LSB matching revisited further denoted as to
+Image Steganography based on LSB matching revisited further denoted as
EAISLSBMR. This approach selects sharper edge
regions with respect
to a given embedding rate: the larger the number of bits to be embedded, the coarser
the edge regions are.
-Then the data hiding algorithm is achieved by applying LSBMR on pixels of these regions.
+Then the data hiding algorithm is achieved by applying LSBMR on some of the pixels of these regions.
The authors show that their proposed method is more efficient than all the LSB, LSBM, and LSBMR approaches
thanks to extensive experiments.
However, it has been shown that the distinguishing error with LSB embedding is lower than
the one with some binary embedding~\cite{DBLP:journals/tifs/FillerJF11}.
-We thus propose to take benefit of these optimized embeddings, provided they are not too time consuming.
+We thus propose to take advantage of these optimized embeddings, provided they are not too time consuming.
In the latter, an hybrid edge detector is presented followed by an ad hoc
embedding.
The Edge detection is computed by combining fuzzy logic~\cite{Tyan1993}
% 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
+ has known great innovations 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,
this research work.
Its main advantage is to be much lighter than the so-called
Highly Undetectable steGO (HUGO) scheme, a well-known state of the art
-steganographic process in spatial domain.
+steganographic process in the spatial domain.
Additionally to this effectiveness,
quite comparable results through noise measures like PSNR-HVS-M,
and weighted PSNR (wPSNR) are obtained.
for minimizing distortion.
After having introduced with details the proposed method,
we have evaluated it through noise measures (namely, the PSNR, PSNR-HVS-M,
-BIQI, and weighted PSNR) and using well-established steganalysers.
+BIQI, and weighted PSNR) and we have used well-established steganalysers.
% Of course, other detectors like the fuzzy edge methods
% deserve much further attention, which is why we intend
systematic replacement of all the LSBs of edges by binary digits provided
by the BBS generator will be investigated, and the consequences of such a
replacement, in terms of security, will be discussed.
-Furthermore, we plan to investigate information hiding on other models, high frequency for JPEG encoding for instance.
+Furthermore, we plan to investigate information hiding on other models, such as high frequency for JPEG encoding.
\bibliographystyle{plain}
the cover pixel selection (Sect.~\ref{sub:edge}),
the adaptive payload considerations (Sect.~\ref{sub:adaptive}),
and how the distortion has been minimized (Sect.~\ref{sub:stc}).
-The message extraction is finally presented (Sect.~\ref{sub:extract}) and a running example ends this section (Sect.~\ref{sub:xpl}).
+The message extraction is then presented (Sect.~\ref{sub:extract}) and a running example ends this section (Sect.~\ref{sub:xpl}).
The flowcharts given in Fig.~\ref{fig:sch}
summarize our steganography scheme denoted by
STABYLO, which stands for STeganography with cAnny, Bbs, binarY embedding at LOw cost.
-What follows are successively details of the inner steps and flows inside
-both the embedding stage (Fig.~\ref{fig:sch:emb})
-and the extraction one (Fig.~\ref{fig:sch:ext}).
+What follows are successively some details of the inner steps and the flows both inside
+ the embedding stage (Fig.~\ref{fig:sch:emb})
+and inside the extraction one (Fig.~\ref{fig:sch:ext}).
Let us first focus on the data embedding.
\begin{figure*}%[t]
\subsection{Security considerations}\label{sub:bbs}
-Among methods of message encryption/decryption
+Among methods of the message encryption/decryption
(see~\cite{DBLP:journals/ejisec/FontaineG07} for a survey)
we implement the Blum-Goldwasser cryptosystem~\cite{Blum:1985:EPP:19478.19501}
that is based on the Blum Blum Shub~\cite{DBLP:conf/crypto/ShubBB82}
As far as fuzzy edge methods are concerned, they are obviously based on fuzzy logic to highlight edges.
Canny filters, on their parts, are an old family of algorithms still remaining a state of the art edge detector. They can be well-approximated by first-order derivatives of Gaussians.
-As the Canny algorithm is well known and studied, fast, and implementable
+As the Canny algorithm is fast, well known, has been studied in depth, and is implementable
on many kinds of architectures like FPGAs, smartphones, desktop machines, and
GPUs, we have chosen this edge detector for illustrative purpose.
Two methods may further be applied to select bits that
will be modified.
The first one randomly chooses the subset of pixels to modify by
-applying the BBS PRNG again. This method is further denoted as to \emph{sample}.
+applying the BBS PRNG again. This method is further denoted as a \emph{sample}.
Once this set is selected, a classical LSB replacement is applied to embed the
stego content.
The second method is a direct application of the
In this example, the cover image is Lena,
which is a $512\times512$ image with 256 grayscale levels.
The message is the poem Ulalume (E. A. Poe), which is constituted by 104 lines, 667
-words, and 3754 characters, \textit{i.e.}, 30032 bits.
+words, and 3,754 characters, \textit{i.e.}, 30,032 bits.
Lena and the first verses are given in Fig.~\ref{fig:lena}.
\begin{figure}
\caption{Cover and message examples} \label{fig:lena}
\end{figure}
-The edge detection returns 18641 and 18455 pixels when $b$ is
+The edge detection returns 18,641 and 18,455 pixels when $b$ is
respectively 7 and 6. These edges are represented in Figure~\ref{fig:edge}.
-Only 9320 bits (resp. 9227 bits) are available for embedding
+Only 9,320 bits (resp. 9,227 bits) are available for embedding
in the former configuration where $b$ is 7 (resp. where $b$ is 6).
In both cases, about the third part of the poem is hidden into the cover.
Results with \emph{adaptive+STC} strategy are presented in
