X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/e556b479316abe0d4183c94c28a84f07f9f64c9c..e477428ed31d945b364bd9064476c1f0a34f86ef:/stc.tex

diff --git a/stc.tex b/stc.tex
index a544278..91f38d7 100644
--- a/stc.tex
+++ b/stc.tex
@@ -1,3 +1,5 @@
+To make this article self-contained, this section recalls
+the basis of the Syndrome Treillis Codes  (STC).
 Let 
 $x=(x_1,\ldots,x_n)$ be the $n$-bits cover vector of the image $X$, 
 $m$ be the message to embed, and 
@@ -17,7 +19,7 @@ Furthermore this code provides a vector $y$ s.t. $Hy$ is equal to
 $m$ for a given binary matrix $H$. 
 
 Let us explain this embedding on a small illustrative example where
-$\rho_X(i,x,y)$ is identically equal to 1,
+$\rho_X(i,x,y)$ is equal to 1,
 whereas $m$ and $x$ are respectively  a 3 bits column
 vector and a 7 bits column vector. 
 Let then $H$ be the binary Hamming matrix  
@@ -60,18 +62,18 @@ $2^n-1$ pixels needs $1-1/2^n$ average changes.
 
 Unfortunately, for any given $H$, finding $y$ that solves $Hy=m$ and  
 that minimizes $D_X(x,y)$, has an exponential complexity with respect to $n$. 
-The Syndrome-Trellis Codes  (STC) 
+The Syndrome-Trellis Codes  
 presented by Filler \emph{et al.} in~\cite{DBLP:conf/mediaforensics/FillerJF10} 
 is a practical solution to this complexity. Thanks to this contribution,
 the solving algorithm has a linear complexity with respect to $n$.
 
 First of all, Filler \emph{et al.} compute the matrix $H$
 by placing a small sub-matrix $\hat{H}$ of size $h × w$ next
-to each other and shifted down by one row. 
+to each other and by shifting down by one row. 
 Thanks to this special form of $H$, one can represent
 every solution of  $m=Hy$ as a path through a trellis.
 
-Next, the  process of finding $y$ consists of two stages: a forward and a backward part.
+Next, the  process of finding $y$ consists in two stages: a forward and a backward part.
 \begin{enumerate}
 \item Forward construction of the trellis that depends on $\hat{H}$, on $x$, on $m$, and on $\rho$.
 \item Backward determination of $y$ that minimizes $D$, starting with