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.
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