QDCT-Based Blind Color Image Watermarking With Aid of GWO and DnCNN for Performance Improvement

Artificial intelligence (AI) is of great potential for improving the performance of image processing and applications. In this study, we incorporate two AI techniques, namely, the grey wolf optimizer (GWO) and denoising convolutional neural network (DnCNN), within a framework developed based on the...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ling-Yuan Hsu, Hwai-Tsu Hu
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Blind color image watermarking grey wolf optimizer denoising convolutional neural network quaternion discrete cosine transform mixed modulation Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/0f5ce558a4b84b948a8ed8eb8cc1652f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:0f5ce558a4b84b948a8ed8eb8cc1652f
record_format	dspace
spelling	oai:doaj.org-article:0f5ce558a4b84b948a8ed8eb8cc1652f2021-11-26T00:01:59ZQDCT-Based Blind Color Image Watermarking With Aid of GWO and DnCNN for Performance Improvement2169-353610.1109/ACCESS.2021.3127917https://doaj.org/article/0f5ce558a4b84b948a8ed8eb8cc1652f2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9614195/https://doaj.org/toc/2169-3536Artificial intelligence (AI) is of great potential for improving the performance of image processing and applications. In this study, we incorporate two AI techniques, namely, the grey wolf optimizer (GWO) and denoising convolutional neural network (DnCNN), within a framework developed based on the quaternion discrete cosine transform (QDCT). Binary embedding is formulated according to the attribute of each QDCT component and the distinctive properties of available modulation schemes. The GWO is responsible for performance optimization, while the DnCNN makes the extracted binary watermark more visually recognizable. Experiment results demonstrate the efficacy of the proposed scheme for resisting a variety of image processing attacks. The proposed scheme outperforms existing ones in terms of the robustness and intelligibility of the retrieved watermarks under the same payload capacity.Ling-Yuan HsuHwai-Tsu HuIEEEarticleBlind color image watermarkinggrey wolf optimizerdenoising convolutional neural networkquaternion discrete cosine transformmixed modulationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 155138-155152 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Blind color image watermarking grey wolf optimizer denoising convolutional neural network quaternion discrete cosine transform mixed modulation Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Blind color image watermarking grey wolf optimizer denoising convolutional neural network quaternion discrete cosine transform mixed modulation Electrical engineering. Electronics. Nuclear engineering TK1-9971 Ling-Yuan Hsu Hwai-Tsu Hu QDCT-Based Blind Color Image Watermarking With Aid of GWO and DnCNN for Performance Improvement
description	Artificial intelligence (AI) is of great potential for improving the performance of image processing and applications. In this study, we incorporate two AI techniques, namely, the grey wolf optimizer (GWO) and denoising convolutional neural network (DnCNN), within a framework developed based on the quaternion discrete cosine transform (QDCT). Binary embedding is formulated according to the attribute of each QDCT component and the distinctive properties of available modulation schemes. The GWO is responsible for performance optimization, while the DnCNN makes the extracted binary watermark more visually recognizable. Experiment results demonstrate the efficacy of the proposed scheme for resisting a variety of image processing attacks. The proposed scheme outperforms existing ones in terms of the robustness and intelligibility of the retrieved watermarks under the same payload capacity.
format	article
author	Ling-Yuan Hsu Hwai-Tsu Hu
author_facet	Ling-Yuan Hsu Hwai-Tsu Hu
author_sort	Ling-Yuan Hsu
title	QDCT-Based Blind Color Image Watermarking With Aid of GWO and DnCNN for Performance Improvement
title_short	QDCT-Based Blind Color Image Watermarking With Aid of GWO and DnCNN for Performance Improvement
title_full	QDCT-Based Blind Color Image Watermarking With Aid of GWO and DnCNN for Performance Improvement
title_fullStr	QDCT-Based Blind Color Image Watermarking With Aid of GWO and DnCNN for Performance Improvement
title_full_unstemmed	QDCT-Based Blind Color Image Watermarking With Aid of GWO and DnCNN for Performance Improvement
title_sort	qdct-based blind color image watermarking with aid of gwo and dncnn for performance improvement
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/0f5ce558a4b84b948a8ed8eb8cc1652f
work_keys_str_mv	AT lingyuanhsu qdctbasedblindcolorimagewatermarkingwithaidofgwoanddncnnforperformanceimprovement AT hwaitsuhu qdctbasedblindcolorimagewatermarkingwithaidofgwoanddncnnforperformanceimprovement
_version_	1718409948819357696

QDCT-Based Blind Color Image Watermarking With Aid of GWO and DnCNN for Performance Improvement

Ejemplares similares