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2D approaches to 3D watermarking: State-of-the-art and perspectives

  • CNRS SAMOVAR UMR 5157
  • University 'Politehnica' of Bucharest

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collectionContribution à une conférenceRevue par des pairs

2 Citations (Scopus)

Résumé

With the advent of the Information Society, video, audio, speech, and 3D media represent the source of huge economic benefits. Consequently, there is a continuously increasing demand for protecting their related intellectual property rights. The solution can be provided by robust watermarking, a research field which exploded in the last 7 years. However, the largest part of the scientific effort was devoted to video and audio protection, the 3D objects being quite neglected. In the absence of any standardisation attempt, the paper starts by summarising the approaches developed in this respect and by further identifying the main challenges to be addressed in the next years. Then, it describes an original oblivious watermarking method devoted to the protection of the 3D objects represented by NURBS (Non-uniform Rational B-Spline) surfaces. Applied to both free form objects and CAD models, the method exhibited very good transparency (no visible differences between the marked and the unmarked model) and robustness (with respect to both traditional attacks and to NURBS processing).

langue originaleAnglais
titreImage Processing
Sous-titreAlgorithms and Systems, Neural Networks, and Machine Learning - Proceedings of SPIE-IS and T Electronic Imaging
EditeurSPIE
ISBN (imprimé)0819461040, 9780819461049
Les DOIs
étatPublié - 1 janv. 2006
Modification externeOui
EvénementImage Processing: Algorithms and Systems, Neural Networks, and Machine Learning - San Jose, CA, États-Unis
Durée: 16 janv. 200618 janv. 2006

Série de publications

NomProceedings of SPIE - The International Society for Optical Engineering
Volume6064
ISSN (imprimé)0277-786X

Une conférence

Une conférenceImage Processing: Algorithms and Systems, Neural Networks, and Machine Learning
Pays/TerritoireÉtats-Unis
La villeSan Jose, CA
période16/01/0618/01/06

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