X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/4d8da06dfcc93848b57c76c1b6cff7ca52f0df4a..fdec47fad10cd693dc899d18ba02c166c1f5349f:/main.tex

diff --git a/main.tex b/main.tex
index 281d2d6..e6405e6 100755
--- a/main.tex
+++ b/main.tex
@@ -83,7 +83,7 @@ Adaptive,  Bbs, and binarY embedding at LOw cost}
 A new steganographic method called STABYLO is introduced in 
 this research work.
 Its main advantage is to be much lighter than the so-called
-HUGO, WOW, and UNIWARDS schemes, the state of the art
+HUGO, WOW, and UNIWARD schemes, the state of the art
 steganographic processes.
 Additionally to this effectiveness, 
 quite comparable results through noise measures like PSNR-HVS-M 
@@ -120,15 +120,26 @@ 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 well-known
-steganographic schemes lite 
+steganographic schemes  
 HUGO, WOW, and UNIWARD.
 This edge-based steganographic approach embeds a Canny
-detection filter, the Blum-Blum-Shub cryptographically secure
-pseudorandom number generator, together with Syndrome-Trellis Codes
+detection filter, the secure Blum-Blum-Shub cryptosystem 
+with its pseudorandom number generator, 
+together with Syndrome-Trellis Codes
 for minimizing distortion.
-After having introduced with details the proposed method,
-we have evaluated it through noise measures (namely, the PSNR, PSNR-HVS-M,
-and weighted PSNR), we have used well-established steganalysers.
+The complexity study of our proposed method and of the  
+state of the art steganographic tools has shown that our approach 
+has the lowest computation cost among all. 
+This justifies the lightweight attribute of our scheme.
+The evaluation of introduced noise measures 
+(namely, the PSNR, PSNR-HVS-M, and weighted PSNR), 
+and of its embedding through stegenalysers (namely Ensemble Classifier)  
+have shown that STABYLO is efficient enough to 
+produce qualitative images and 
+to face steganalysers.
+
+
+
 
 % Of course, other detectors like the fuzzy edge methods
 % deserve much further attention, which is why we intend