NDLI: Weighted Extreme Learning Machine for Digital Watermarking in DWT Domain

Content Provider	IEEE Xplore Digital Library
Author	Singh, Ram Pal Dabas, Neelam Nagendra Chaudhary, Vikash
Copyright Year	2015
Abstract	In this paper, a digital watermarking scheme has been implemented using Weighted Extreme Learning Machine (WELM) on images and results are compared with other existing methods. The neighbourhood relationship among the pixel in image can be used as an reference positions, WELM is used as a regressor. Digital watermarking problem can be treated as regression problem can be trained at the embedding procedure and watermark or logo or sequence can be embedded. The watermark as an information can be embedded into blue channel of input images used for watermarking taking into account of human vision system (HSV). As WELM algorithm is very fast, cost sensitive and has good learning and generalization ability, the watermark can be correctly extracted despite of the watermarked image subject to several malicious attacks. Experimental results show that the WELM based watermarking scheme outperformed other existing methods against different attacks including salt & peppers (0:04), scaling 50%, cropping 15%, rotation 150 etc. As implemented digital watermarking scheme is robust and imperceptible determined based on calculated metrics PSNR, BER.
Starting Page	393
Ending Page	396
File Size	212740
Page Count	4
File Format	PDF
e-ISBN	9781509001880
DOI	10.1109/IIH-MSP.2015.64
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-23
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Bit error rate Watermarking Color Robustness Multimedia communication Data mining
Content Type	Text
Resource Type	Article

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