X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/771c241658964b134e9ee35cba6f6fc35dbc2ea0..c3438d8c47f3989f6d0d0e6109a3c216cca0d7c7:/experiments.tex

diff --git a/experiments.tex b/experiments.tex
index c92062c..21d813d 100644
--- a/experiments.tex
+++ b/experiments.tex
@@ -16,7 +16,7 @@ The message length is thus defined to be the half of this set cardinality.
 In this strategy, two methods are thus applied to extract bits that 
 are modified. The first one is a direct application of the STC algorithm.
 This method is further referred as \emph{adaptive+STC}.
-The second one randomly choose the subset of pixels to modify by 
+The second one randomly chooses the subset of pixels to modify by 
 applying the BBS PRNG again. This method is denoted \emph{adaptive+sample}.
 Notice that the rate between 
 available bits  and bit message length is always equal to 2.
@@ -30,7 +30,7 @@ Its corresponds to an  average payload of 6.35\%.
 
 
 In the latter, the embedding rate is defined as a percentage between the 
-number of the modified pixels and the length of the bit message.
+number of modified pixels and the length of the bit message.
 This is the classical approach adopted in steganography.
 Practically, the Canny algorithm generates a 
 a set of edge pixels with threshold that is decreasing until its cardinality
@@ -44,12 +44,12 @@ Otherwise, pixels are again randomly chosen with BBS.
 The visual quality of the STABYLO scheme is evaluated in this section.
 For the sake of completeness, four metrics are computed in these experiments: 
 the Peak Signal to Noise Ratio (PSNR), 
-the PSNR-HVS-M family~\cite{PSECAL07,psnrhvsm11} , 
+the PSNR-HVS-M family~\cite{PSECAL07,psnrhvsm11}, 
 the BIQI~\cite{MB10,biqi11}, and 
 the weighted PSNR (wPSNR)~\cite{DBLP:conf/ih/PereiraVMMP01}.
 The first one is widely used but does not take into
 account the Human Visual System (HVS).
-The other last ones have been designed to tackle this problem.
+The other ones have been designed to tackle this problem.
 
 \begin{table}
 \begin{center}
@@ -78,9 +78,9 @@ 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.
 Numerical results are indeed always greater for the former strategy than 
-for the latter, except for the BIQI metrics where differences are not relevant.
+for the latter, except for the BIQI metrics where differences are not really relevant.
 These results are not surprising since the adaptive strategy aims at 
-embedding messages whose length is decided according to a higher threshold
+embedding messages whose length is decided according to an higher threshold
 into the edge detection.  
 Let us focus on the quality of HUGO images: with a given fixed 
 embedding rate (10\%), 
@@ -96,7 +96,7 @@ scheme detailed in~\cite{Luo:2010:EAI:1824719.1824720}
 executed with a 10\% embedding rate 
 has the same PSNR but a lower wPSNR than ours:
 these two metrics are respectively equal to 61.9 and 68.9. 
-Next both the approaches~\cite{DBLP:journals/eswa/ChenCL10,Chang20101286}
+Next, both the approaches~\cite{DBLP:journals/eswa/ChenCL10,Chang20101286}
 focus on increasing the payload while the PSNR is acceptable, but do not 
 give quality metrics for fixed embedding rate from a large base of images. 
 Our approach outperforms the former thanks to the introduction of the STC 
@@ -119,9 +119,9 @@ Parameters are firstly estimated and an adaptive Asymptotically Uniformly Most P
 (AUMP) test is designed (theoretically and practically), to check whether
 an image has stego content or not.  
 In the latter, the authors show that the 
-machine learning step, (which is often
-implemented as support vector machine)
-can be favorably executed thanks to an Ensemble Classifiers.
+machine learning step, which is often
+implemented as support vector machine,
+can be favorably executed thanks to an ensemble classifier.
 
 
 
@@ -148,5 +148,6 @@ Ensemble Classifier & 0.47 & 0.44 & 0.35     & 0.48     \\
 
 Results show that our approach is more easily detectable than HUGO, which
 is the most secure steganographic tool, as far as we know. However due to its 
-huge number of features integration, it is not lightweight.   
+huge number of features integration, it is not lightweight, which justifies 
+in authors' opinion the consideration of the proposed method.