NDLI: A new adjustable blind watermarking based on GA and SVD

Content Provider	IEEE Xplore Digital Library
Author	Modaghegh, H. Khosravi R, H. Akbarzadeh-T
Copyright Year	2009
Description	Author affiliation: Department of Electrical Engineering, Ferdowsi University Of Mashhad , Iran (Modaghegh, H.; Khosravi R, H.; Akbarzadeh-T)
Abstract	As information technology and multimedia products become more and more readily available, copyright and other related legal topics become more and more significant. Embedding copyright information as hidden data into the multimedia product-named watermarking- is one of the methods to protect owner rights. Two main concepts in watermarking are imperceptibility and robustness of the watermark. A tradeoff between these two features exists, which can be introduced as an optimization problem. Genetic Algorithm (GA) is applied to solve this optimization problem. In this paper, a new adjustable watermarking method based on singular value decomposition is presented so that SVD parameters are adjusted by using the GA considering image complexity and attack resistance. The proposed watermarking method is also an adjustable solution, so that by changing fitness function (cost function), watermarking method can be converted to each of robust, fragile, or semi-fragile types. Simulation results show that the proposed method has better results from the case where watermarking parameters are adjusted by the user empirically.
Starting Page	6
Ending Page	10
File Size	2248918
Page Count	5
File Format	PDF
ISBN	9781424456987
e-ISBN	9781424457007
DOI	10.1109/IIT.2009.5413374
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-15
Publisher Place	United Arab Emirates
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	SVD Law Multimedia systems Watermarking Information technology Videos Genetic algorithms blind Watermarking Robustness GA Protection Legal factors Singular value decomposition
Content Type	Text
Resource Type	Article

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