Neuro Fuzzy Architecture for Gray Scale Image Watermarking using Fractal Dimensions

Anurag Mishra, Khushwant Sehra, Girija CHETTY

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

3 Citations (Scopus)

Abstract

Image processing applications in general consume more time. Same is true for image watermarking as well. For this purpose, it is crucial to keep in mind the image quality and the running time spans post processing. In this paper, a novel neuro fuzzy architecture for gray-scale image watermarking using fractal dimensions is proposed. The development also involves the tradeoff between the imperceptibility and robustness of the watermark embedding scheme. To this end, a very fast single layer feed forward neural network known as Extreme Learning Machine (ELM) is employed for image watermarking. The neural machine is combined with a Mamdani Fuzzy Inference System (FIS) to build a Neuro– Fuzzy architecture. The analysis is carried out in the initial stages using fractal analysis. The robustness of the scheme is evaluated by subjecting the signed images to StirMark attacks.
Original languageEnglish
Title of host publicationProceedings 2018 International Joint Conference on Neural Networks (IJCNN)
EditorsCesare Alippi, Patricia Melin, Chuan-Kang Ting
Place of PublicationBrazil
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-8
Number of pages8
Volume2018-July
ISBN (Electronic)9781509060146
ISBN (Print)9781509060153
DOIs
Publication statusPublished - 8 Jul 2018
Event2018 IEEE International Joint Conference on Neural Networks (IJCNN) : In conjunction with the 2018 World Congress on Computational Intelligence (WCCI 2018) - Rio de Janeiro, Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018
http://www.ecomp.poli.br/~wcci2018/

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2018-July

Conference

Conference2018 IEEE International Joint Conference on Neural Networks (IJCNN)
Abbreviated titleIJCNN 2018
CountryBrazil
CityRio de Janeiro
Period8/07/1813/07/18
Internet address

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