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 contribution

2 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)
Place of PublicationRio de Janeiro, Brazil, Brazil
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
EventIEEE International Conference on Neural Networks -
Duration: 1 Jan 2011 → …

Publication series

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

Conference

ConferenceIEEE International Conference on Neural Networks
Abbreviated titleICNN
Period1/01/11 → …

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  • Cite this

    Mishra, A., Sehra, K., & CHETTY, G. (2018). Neuro Fuzzy Architecture for Gray Scale Image Watermarking using Fractal Dimensions. In Proceedings 2018 International Joint Conference on Neural Networks (IJCNN) (Vol. 2018-July, pp. 1-8). [8489350] (Proceedings of the International Joint Conference on Neural Networks; Vol. 2018-July). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2018.8489350