NDLI: An adaptive, SVM -based watermarking in frequency domain

Content Provider	IEEE Xplore Digital Library
Author	Feng Yang Lei Li
Copyright Year	2008
Description	Author affiliation: Inst. of Autom., Nanjing Univ. of Posts & Telecommun., Nanjing (Feng Yang; Lei Li)
Abstract	In digital management, multimedia content and data can easily be used in an illegal way - being copied, modified and distributed again. Copyright protection, intellectual and material rights protection for authors, owners, buyers, distributors and the authenticity of content are crucial factors in solving an urgent and real problem. In this paper, we describe an algorithm of watermark-embedding in frequency domain based on the support vector machine (SVC). Because of the SVC has superiority in classified-area, so we use it to simulate the picturespsila texture-characteristic and separate the picturepsilas sub-blocks into two classes, in this way, we choose the embedding intensity adaptively. Firstly, we separate the picture into many sub-blocks (8*8) , then we use their texture characteristics as the training sample to train the SVC, the training goal is -1 and 1 (represents two kinds of different picture sub-blocks); Secondly, we use the training result to establish a SVC classification model, then we can use this model to classify the new picturepsilas blocks and decided the embedding intensity adaptively. The simulation result indicates that the watermarked-pictures with this method have a good visual sensation and the watermark is robust to many common attacks.
Starting Page	465
Ending Page	469
File Size	290167
Page Count	5
File Format	PDF
ISBN	9781424422388
DOI	10.1109/ICWAPR.2008.4635825
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Static VAr compensators Watermarking Robustness Classification algorithms SVM Mathematical model Watermark
Content Type	Text
Resource Type	Article

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