NDLI: Lagrangian twin support vector regression and genetic algorithm based robust grayscale image watermarking

Content Provider	Springer Nature Link
Author	Yadav, Ashok Kumar Mehta, Rajesh Kumar, Raj Vishwakarma, Virendra P.
Copyright Year	2016
Abstract	A novel imperceptible, secure and robust grayscale image watermarking scheme using Lagrangian twin support vector regression (LTSVR) and genetic algorithm (GA) in discrete Cosine transform (DCT) domain is presented in this manuscript. Fuzzy entropy is used to select the relevant blocks for embedding the watermark. Selected number of blocks based on fuzzy entropy not only reduces the dimensionality of the watermarking problem but also discards redundant and irrelevant blocks. Significant DCT coefficients having high energy compaction property of each selected block are used to form the image dataset to train LTSVR to find the non-linear regression function between the input and target vector. The adaptive watermark strength, different for each selected block, is decided by the GA process based on well defined fitness function. Due to good learning capability of image characteristics and high generalization property of LTSVR, watermark is successfully extracted from the watermarked images against a series of image processing operations. From the experimental and comparison results performed on standard and real world images, it is inferred that the proposed method is suitable for copyright protection applications where high degree of robustness is desirable.
Starting Page	9371
Ending Page	9394
Page Count	24
File Format	PDF
ISSN	13807501
Journal	Multimedia Tools and Applications
Volume Number	75
Issue Number	15
e-ISSN	15737721
Language	English
Publisher	Springer US
Publisher Date	2016-03-09
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	DCT Genetic algorithm Lagrangian Twin SVR Digital Image Watermarking Multimedia Information Systems Computer Communication Networks Data Structures, Cryptology and Information Theory Special Purpose and Application-Based Systems
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software Media Technology

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