DocumentCode
3207526
Title
A hierarchical spatially adaptive image model for perceptual mask design and multiplicative watermark detection
Author
Mairgiotis, Antonis ; Galatsanos, Nikolaos
Author_Institution
Dept. of Comput. Sci., Univ. of Ioannina, Ioannina, Greece
fYear
2009
fDate
6-9 Dec. 2009
Firstpage
6
Lastpage
10
Abstract
In this work motivated by a hierarchical spatially adaptive image prior that we have developed for additive watermarking; we first, propose a new perceptual mask which improves robustness of additive watermark detectors in the spatial domain. The proposed mask is based on the local image variations along the two principal directions and enhances the watermark´s energy while satisfying the imperceptibility requirement. Second we extent the application of the proposed hierarchical image prior for the multiplicative watermarking problem and we propose new watermark detectors. Numerical results are provided that demonstrate both the value of the proposed mask, and the improved sensitivity as compared to additive watermarking, with the same image model, of the proposed multiplicative watermark detector. Furthermore, we demonstrate its improved robustness compared to other state of the art similar in spirit watermark detectors.
Keywords
image coding; numerical analysis; object detection; watermarking; hierarchical spatially adaptive image model; imperceptibility requirement; local image variations; multiplicative watermark detection; perceptual mask design; Additives; Computer science; Detectors; Discrete cosine transforms; Discrete wavelet transforms; Humans; Robustness; Testing; Visual system; Watermarking; hierarchical spatially adaptive image model; multiplicative watermarking; perceptual masking;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Forensics and Security, 2009. WIFS 2009. First IEEE International Workshop on
Conference_Location
London
Print_ISBN
978-1-4244-5279-8
Electronic_ISBN
978-1-4244-5280-4
Type
conf
DOI
10.1109/WIFS.2009.5386494
Filename
5386494
Link To Document