Pixel type classification based reversible data hiding for hyperspectral images

doi:10.1016/j.knosys.2022.109606

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	Pixel type classification based reversible data hiding for hyperspectral images
	Fan, Guojun 2; Pan, Zhibin 2; Zhou, Quan 1; Dong, Jing 3; Zhang, Xiaoran 2
刊名	KNOWLEDGE-BASED SYSTEMS
	2022-10-27
卷号	254 页码:11
关键词	Hyperspectral images Pixel classifying Adaptive prediction Three -components complexity Reversible data hiding
ISSN号	0950-7051
DOI	10.1016/j.knosys.2022.109606
通讯作者	Pan, Zhibin(zbpan@mail.xjtu.edu.cn)
英文摘要	The digital files are becoming larger and larger with the development of computer hardware and computing power, and the fast processing for large files, e.g., hyperspectral images, is becoming feasible for not only companies but also individuals. However, the acquirement of hyperspectral images still costs a lot. Therefore, security issues like copyright ownership of hyperspectral images need to be taken seriously. Reversible data hiding (RDH) is a technology that can embed watermarking information into multimedia cover to protect copyright. However, the natural-images-based RDH methods cannot exploit the large amount of inter-band redundancy contained by hyperspectral images, which leads to a low efficiency for copyright protection. In this paper, a novel RDH method specially designed for hyperspectral images is proposed. We use the value information from the pixel of an adjacent band to classify each pixel into one of the five types when predicting it, and an adaptive predictor is matched for the pixels of each type to achieve a high prediction accuracy. Finally, a complexity calculation method containing three components is put forward to further improve the embedding performance. Experiments show that the proposed method outperforms the existing RDH method for hyperspectral images and other state-of-the-art RDH methods for natural images. (C) 2022 Elsevier B.V. All rights reserved.
资助项目	National Natural Science Foundation of China[U1903213] ; Open Project of the National Laboratory of Pattern Recognition[202100033] ; Open Foundation of Henan Key Laboraty of Cyberspace Situation Awareness[HNTS2022015] ; Zhejiang Provincial Commonweal Project[LGF21F030002]
WOS关键词	LOSSLESS DATA ; PREDICTION ; EXPANSION ; WATERMARKING
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000861031200010
资助机构	National Natural Science Foundation of China ; Open Project of the National Laboratory of Pattern Recognition ; Open Foundation of Henan Key Laboraty of Cyberspace Situation Awareness ; Zhejiang Provincial Commonweal Project
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/50419]
专题	自动化研究所_智能感知与计算研究中心
通讯作者	Pan, Zhibin
作者单位	1.CAST, Natl Key Lab Sci & Technol Space Microwave, Xian 710100, Peoples R China 2.Xi An Jiao Tong Univ, Fac Elect & Informat Engn, Xian 710049, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Fan, Guojun,Pan, Zhibin,Zhou, Quan,et al. Pixel type classification based reversible data hiding for hyperspectral images[J]. KNOWLEDGE-BASED SYSTEMS,2022,254:11.
APA	Fan, Guojun,Pan, Zhibin,Zhou, Quan,Dong, Jing,&Zhang, Xiaoran.(2022).Pixel type classification based reversible data hiding for hyperspectral images.KNOWLEDGE-BASED SYSTEMS,254,11.
MLA	Fan, Guojun,et al."Pixel type classification based reversible data hiding for hyperspectral images".KNOWLEDGE-BASED SYSTEMS 254(2022):11.