The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Nous présentons une nouvelle approche pour développer des modèles de décodage basés sur l'apprentissage automatique (ML) pour extraire un filigrane en présence d'attaques. La caractérisation statistique des composantes de différentes bandes de fréquences est exploitée pour permettre une extraction aveugle du filigrane. Les résultats expérimentaux montrent que le schéma de décodage proposé basé sur le ML peut s'adapter à l'application de filigrane en apprenant les modifications dans l'espace des fonctionnalités induites par l'attaque utilisée.
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Asifullah KHAN, Syed Fahad TAHIR, Tae-Sun CHOI, "Intelligent Extraction of a Digital Watermark from a Distorted Image" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 7, pp. 2072-2075, July 2008, doi: 10.1093/ietisy/e91-d.7.2072.
Abstract: We present a novel approach to developing Machine Learning (ML) based decoding models for extracting a watermark in the presence of attacks. Statistical characterization of the components of various frequency bands is exploited to allow blind extraction of the watermark. Experimental results show that the proposed ML based decoding scheme can adapt to suit the watermark application by learning the alterations in the feature space incurred by the attack employed.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.7.2072/_p
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@ARTICLE{e91-d_7_2072,
author={Asifullah KHAN, Syed Fahad TAHIR, Tae-Sun CHOI, },
journal={IEICE TRANSACTIONS on Information},
title={Intelligent Extraction of a Digital Watermark from a Distorted Image},
year={2008},
volume={E91-D},
number={7},
pages={2072-2075},
abstract={We present a novel approach to developing Machine Learning (ML) based decoding models for extracting a watermark in the presence of attacks. Statistical characterization of the components of various frequency bands is exploited to allow blind extraction of the watermark. Experimental results show that the proposed ML based decoding scheme can adapt to suit the watermark application by learning the alterations in the feature space incurred by the attack employed.},
keywords={},
doi={10.1093/ietisy/e91-d.7.2072},
ISSN={1745-1361},
month={July},}
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TY - JOUR
TI - Intelligent Extraction of a Digital Watermark from a Distorted Image
T2 - IEICE TRANSACTIONS on Information
SP - 2072
EP - 2075
AU - Asifullah KHAN
AU - Syed Fahad TAHIR
AU - Tae-Sun CHOI
PY - 2008
DO - 10.1093/ietisy/e91-d.7.2072
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2008
AB - We present a novel approach to developing Machine Learning (ML) based decoding models for extracting a watermark in the presence of attacks. Statistical characterization of the components of various frequency bands is exploited to allow blind extraction of the watermark. Experimental results show that the proposed ML based decoding scheme can adapt to suit the watermark application by learning the alterations in the feature space incurred by the attack employed.
ER -