Neuro Fuzzy Architecture for Gray Scale Image Watermarking using Fractal Dimensions

Anurag Mishra; Khushwant Sehra; Girija CHETTY

doi:10.1109/IJCNN.2018.8489350

Neuro Fuzzy Architecture for Gray Scale Image Watermarking using Fractal Dimensions

Anurag Mishra, Khushwant Sehra, Girija CHETTY

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

5 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 language	English
Title of host publication	Proceedings 2018 International Joint Conference on Neural Networks (IJCNN)
Editors	Cesare Alippi, Patricia Melin, Chuan-Kang Ting
Place of Publication	Brazil
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1-8
Number of pages	8
Volume	2018-July
ISBN (Electronic)	9781509060146
ISBN (Print)	9781509060153
DOIs	https://doi.org/10.1109/IJCNN.2018.8489350
Publication status	Published - 8 Jul 2018
Event	2018 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 2018 → 13 Jul 2018 http://www.ecomp.poli.br/~wcci2018/

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2018-July

Conference

Conference	2018 IEEE International Joint Conference on Neural Networks (IJCNN)
Abbreviated title	IJCNN 2018
Country/Territory	Brazil
City	Rio de Janeiro
Period	8/07/18 → 13/07/18
Internet address	http://www.ecomp.poli.br/~wcci2018/

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10.1109/IJCNN.2018.8489350

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Mishra, A., Sehra, K., & CHETTY, G. (2018). Neuro Fuzzy Architecture for Gray Scale Image Watermarking using Fractal Dimensions. In C. Alippi, P. Melin, & C-K. Ting (Eds.), 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

Mishra, Anurag ; Sehra, Khushwant ; CHETTY, Girija. / Neuro Fuzzy Architecture for Gray Scale Image Watermarking using Fractal Dimensions. Proceedings 2018 International Joint Conference on Neural Networks (IJCNN). editor / Cesare Alippi ; Patricia Melin ; Chuan-Kang Ting. Vol. 2018-July Brazil : IEEE, Institute of Electrical and Electronics Engineers, 2018. pp. 1-8 (Proceedings of the International Joint Conference on Neural Networks).

@inproceedings{14f394eac3814075b7e724176aa1dbdb,

title = "Neuro Fuzzy Architecture for Gray Scale Image Watermarking using Fractal Dimensions",

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.",

keywords = "Watermarking, Fuzzy Logic, Extreme Learning Machine (ELM), StirMark Benchmarks, Human Visual System (HVS), PSNR, SSIM, Fractal Dimensions, NC, Mamdani Fuzzy Inference System (FIS), BER, Discrete Cosine Transform (DCT)",

author = "Anurag Mishra and Khushwant Sehra and Girija CHETTY",

year = "2018",

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doi = "10.1109/IJCNN.2018.8489350",

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volume = "2018-July",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

pages = "1--8",

editor = "Cesare Alippi and Patricia Melin and Chuan-Kang Ting",

booktitle = "Proceedings 2018 International Joint Conference on Neural Networks (IJCNN)",

address = "United States",

note = "2018 IEEE International Joint Conference on Neural Networks (IJCNN) : In conjunction with the 2018 World Congress on Computational Intelligence (WCCI 2018), IJCNN 2018 ; Conference date: 08-07-2018 Through 13-07-2018",

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Mishra, A, Sehra, K & CHETTY, G 2018, Neuro Fuzzy Architecture for Gray Scale Image Watermarking using Fractal Dimensions. in C Alippi, P Melin & C-K Ting (eds), Proceedings 2018 International Joint Conference on Neural Networks (IJCNN). vol. 2018-July, 8489350, Proceedings of the International Joint Conference on Neural Networks, vol. 2018-July, IEEE, Institute of Electrical and Electronics Engineers, Brazil, pp. 1-8, 2018 IEEE International Joint Conference on Neural Networks (IJCNN) , Rio de Janeiro, Brazil, 8/07/18. https://doi.org/10.1109/IJCNN.2018.8489350

Neuro Fuzzy Architecture for Gray Scale Image Watermarking using Fractal Dimensions. / Mishra, Anurag; Sehra, Khushwant; CHETTY, Girija.
Proceedings 2018 International Joint Conference on Neural Networks (IJCNN). ed. / Cesare Alippi; Patricia Melin; Chuan-Kang Ting. Vol. 2018-July Brazil: IEEE, Institute of Electrical and Electronics Engineers, 2018. p. 1-8 8489350 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2018-July).

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

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T1 - Neuro Fuzzy Architecture for Gray Scale Image Watermarking using Fractal Dimensions

AU - Mishra, Anurag

AU - Sehra, Khushwant

AU - CHETTY, Girija

PY - 2018/7/8

Y1 - 2018/7/8

N2 - 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.

AB - 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.

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KW - Fuzzy Logic

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KW - StirMark Benchmarks

KW - Human Visual System (HVS)

KW - PSNR

KW - SSIM

KW - Fractal Dimensions

KW - NC

KW - Mamdani Fuzzy Inference System (FIS)

KW - BER

KW - Discrete Cosine Transform (DCT)

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DO - 10.1109/IJCNN.2018.8489350

M3 - Conference contribution

SN - 9781509060153

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T3 - Proceedings of the International Joint Conference on Neural Networks

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BT - Proceedings 2018 International Joint Conference on Neural Networks (IJCNN)

A2 - Alippi, Cesare

A2 - Melin, Patricia

A2 - Ting, Chuan-Kang

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Brazil

T2 - 2018 IEEE International Joint Conference on Neural Networks (IJCNN)

Y2 - 8 July 2018 through 13 July 2018

ER -

Mishra A, Sehra K, CHETTY G. Neuro Fuzzy Architecture for Gray Scale Image Watermarking using Fractal Dimensions. In Alippi C, Melin P, Ting C-K, editors, Proceedings 2018 International Joint Conference on Neural Networks (IJCNN). Vol. 2018-July. Brazil: IEEE, Institute of Electrical and Electronics Engineers. 2018. p. 1-8. 8489350. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN.2018.8489350

Neuro Fuzzy Architecture for Gray Scale Image Watermarking using Fractal Dimensions

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