\title{STABYLO:
STeganography with
-Adaptive, Bbs, and binarY embedding at LOw cost.}
+Adaptive, Bbs, and binarY embedding at LOw cost}
\author{Jean-Fran\c cois Couchot, Raphael Couturier, and Christophe Guyeux\thanks{Authors in alphabetic order}}
A new steganographic method called STABYLO is introduced in
this research work.
Its main advantage is to be much lighter than the so-called
-Highly Undetectable steGO (HUGO) scheme, a well-known state of the art
-steganographic process in the spatial domain.
+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,
+quite comparable results through noise measures like PSNR-HVS-M
and weighted PSNR (wPSNR) are obtained.
To achieve the proposed goal, famous experimented
components of signal processing,
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.
+somewhat smaller, security than well-known
+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,
-BIQI, 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
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.
+all the existing edge detection methods,
+to see if the STABYLO evaluation scores can
+be improved by replacing Canny with another edge filter.
+Moreover, we plan to improve the distortion function by integrating
+into a numerical cost the gradient value of this kind of
+algorithm. We could thus transmit this value to STC contrary to the current
+version where the distortion that is transmited is either 1 in
+the adaptive strategy or 1,10, 100 in the fixed strategy.
+
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
Furthermore, we plan to investigate information hiding on other models, such as high frequency for JPEG encoding.
-\bibliographystyle{compj}
+\bibliographystyle{spbasic}
\bibliography{abbrev,biblioand}
\end{document}
-
-
