We restrict experiments to
this set of cover images since this paper is more focused on
the methodology than benchmarking.
+We use the matrices given in table~\ref{table:matrices:H}
+as introduced in~\cite{}, since these ones have experimentally
+be proven to have the best modification efficiency.
+
+\begin{table}
+$$
+\begin{array}{|l|l|}
+\textrm{rate} & \textrm{matrix generators} \\
+$\frac{1}{2} & \{71,109\}
+$\frac{1}{3} & \{95, 101, 121\}
+$\frac{1}{4} & \{81, 95, 107, 121\}
+$\frac{1}{5} & \{75, 95, 97, 105, 117\}
+$\frac{1}{6} & \{73, 83, 95, 103, 109, 123\}
+$\frac{1}{7} & \{69, 77, 93, 107, 111, 115, 121\}
+$\frac{1}{8} & \{69, 79, 81, 89, 93, 99, 107, 119\}
+$\frac{1}{9} & \{69, 79, 81, 89, 93, 99, 107, 119, 125]
+
+
+
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
the $+1$ or $-1$ is randomly added to the cover pixel LSB value
only if this one does not correspond to the secret bit.
%TODO : modifier ceci
-By considering well-encrypted hidden messages, probabilities of increasing or ofdecreasing value of pixels are equal. Then usual statistical approaches
+By considering well-encrypted hidden messages, probabilities of increasing or of decreasing value of pixels are equal. Then 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/Ker05,FK12},
+The most accurate detectors for this matching are universal steganalysers such as~\cite{LS08,DBLP:conf/ih/Ker05,FK12},
which classify images according to extracted features from neighboring elements of residual noise.
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 the proposed steganographic scheme and applies it on a running example.
-Section~\ref{sec:experiments} shows experiments on image quality, steganalytic evaluation, complexity of our approach,
-and compare them to the state of the art steganographic schemes.
+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 compare it to the HUGO's one.
+Section~\ref{sec:experiments} shows experiments on image quality, steganalysis evaluation, and compare them to the state of the art steganographic schemes.
Finally, concluding notes and future work are given in Section~\ref{sec:concl}.
\title[STABYLO]{STABYLO:
-a lightweight edge-based steganographic approach}
+STeganography with
+Adaptive, Bbs, and binarY embedding at LOw cost.}
\author{Jean-Fran\c cois Couchot, Raphael Couturier, and Christophe Guyeux\thanks{Authors in alphabetic order}}
\input{ourapproach.tex}
+\section{Complexity Analysis}\label{sub:complexity}
+\input{comlexity}
+
\section{Experiments}\label{sec:experiments}
\input{experiments}
\section{Conclusion}\label{sec:concl}
-The STABYLO algorithm, whose acronym means STeganography
-with cAnny, Bbs, binarY embedding at LOw cost, has been introduced
+The STABYLO algorithm, whose acronym means STeganography with
+Adaptive, Bbs, and binarY embedding at LOw cost, has been introduced
in this document as an efficient method having comparable, though
somewhat smaller, security than the well-known
Highly Undetectable steGO (HUGO) steganographic scheme.
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 we have used well-established steganalysers.
+BIQI, and weighted PSNR), we have used well-established steganalysers.
% Of course, other detectors like the fuzzy edge methods
% deserve much further attention, which is why we intend
% to investigate systematically all of these detectors in our next work.
-c
-
For future work, the authors' intention is to investigate systematically
all the existing edge detection methods, to see if the STABYLO evaluation scores can
be improved by replacing Canny with another edge filter.
-% We will try
-% to take into account the least significant bits too during all the
-% stages of the algorithm, hoping by doing so to be closer to the HUGO scores against
-% steganalyzers.
Other steganalysers than the ones used in this document will be
examined for the sake of completeness. Finally, the
systematic replacement of all the LSBs of edges by binary digits provided
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.
+STABYLO, which stands for STeganography with
+Adaptive, Bbs, binarY embedding at LOw cost.
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}).
-\section{Complexity Analysis}\label{sub:complexity}
-This section aims at justifying the leightweight attribute of our approach.
-To be more precise, we compare the complexity of our schemes to the
-state of the art steganography, namely HUGO~\cite{DBLP:conf/ih/PevnyFB10}.
-
-
-In what folllows, we consider an $n \times n$ square image.
-First of all, HUGO starts with computing the second order SPAM Features.
-This steps is in $O(n^2 + 2.343^2)$ due to the calculation
-of the difference arrays and next of the 686 features (of size 343).
-Next for each pixel, the distortion measure is calculated by +1/-1 modifying
-its value and computing again the SPAM
-features. Pixels are thus selected according to their ability to provide
-an image whose SPAM features are close to the original one.
-The algorithm is thus computing a distance between each Feature,
-which is at least in $O(343)$ and an overall distance between these
-metrics which is in $O(686)$. Computing the distance is thus in
-$O(2\time 343^2)$ and this mdification is thus in $O(2\time 343^2 \time n^2)$.
-Ranking these results may be achieved with a insertion sort which is in $2.n^2 \ln(n)$.
-The overall complexity of the pixel selection is thus
-$O(n^2 +2.343^2 + 2\time 343^2 \time n^2 + 2.n^2 \ln(n))$, \textit{i.e}
-$O(2.n^2(343^2 + \ln(n)))$.
-
-Our edge selection is based on a Canny Filter,
-whose complexity is in $O(2n^2.\ln(n))$ thanks to the convolution step
-which can be implemented with FFT.
-The complexity of Hugo is at least $343^2/\ln{n}$ times higher than our scheme.
-
-
-
-
-
