An Improved Perceptual Hash Algorithm Based on U-Net for the Authentication of High-Resolution Remote Sensing Image

doi:10.3390/app9152972

	An Improved Perceptual Hash Algorithm Based on U-Net for the Authentication of High-Resolution Remote Sensing Image
	Ding, Kaimeng 1,3; Yang, Zedong 2; Wang, Yingying 1; Liu, Yueming 3
刊名	APPLIED SCIENCES-BASEL
	2019-08-01
卷号	9 期号:15 页码:20
关键词	high-resolution remote sensing image deep learning perceptual hash integrity authentication U-net
DOI	10.3390/app9152972
通讯作者	Ding, Kaimeng(dkm@jit.edu.cn) ; Yang, Zedong(yaogandasai@163.com)
英文摘要	Data security technology is of great significance for the effective use of high-resolution remote sensing (HRRS) images in GIS field. Integrity authentication technology is an important technology to ensure the security of HRRS images. Traditional authentication technologies perform binary level authentication of the data and cannot meet the authentication requirements for HRRS images, while perceptual hashing can achieve perceptual content-based authentication. Compared with traditional algorithms, the existing edge-feature-based perceptual hash algorithms have already achieved high tampering authentication accuracy for the authentication of HRRS images. However, because of the traditional feature extraction methods they adopt, they lack autonomous learning ability, and their robustness still exists and needs to be improved. In this paper, we propose an improved perceptual hash scheme based on deep learning (DL) for the authentication of HRRS images. The proposed method consists of a modified U-net model to extract robust feature and a principal component analysis (PCA)-based encoder to generate perceptual hash values for HRRS images. In the training stage, a training sample generation method combining artificial processing and Canny operator is proposed to generate robust edge features samples. Moreover, to improve the performance of the network, exponential linear unit (ELU) and batch normalization (BN) are applied to extract more robust and accurate edge feature. The experiments have shown that the proposed algorithm has almost 100% robustness to format conversion between TIFF and BMP, LSB watermark embedding and lossless compression. Compared with the existing algorithms, the robustness of the proposed algorithm to lossy compression has been improved, with an average increase of 10%. What is more, the algorithm has good sensitivity to detect local subtle tampering to meet the high-accuracy requirements of authentication for HRRS images.
资助项目	National Key R&D Program of China[2016YFF0204006] ; National Natural Science Foundation of China[41801303] ; Jiangsu Province Science and Technology Support Program[BK20170116] ; Scientific Research Hatch Fund of Jinling Institute of Technology[jit-fhxm-201604] ; Scientific Research Hatch Fund of Jinling Institute of Technology[jit-b-201520] ; Qing Lan Project
WOS关键词	DIGITAL SIGNATURE ; TRANSFORM
WOS研究方向	Chemistry ; Materials Science ; Physics
语种	英语
出版者	MDPI
WOS记录号	WOS:000482134500027
资助机构	National Key R&D Program of China ; National Natural Science Foundation of China ; Jiangsu Province Science and Technology Support Program ; Scientific Research Hatch Fund of Jinling Institute of Technology ; Qing Lan Project
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/69711]
专题	中国科学院地理科学与资源研究所
通讯作者	Ding, Kaimeng; Yang, Zedong
作者单位	1.Jinling Inst Technol, Nanjing 211169, Jiangsu, Peoples R China 2.Chinese Acad Surveying & Mapping, Beijing 100039, Peoples R China 3.Chinese Acad Sci, State Key Lab Resource & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Ding, Kaimeng,Yang, Zedong,Wang, Yingying,et al. An Improved Perceptual Hash Algorithm Based on U-Net for the Authentication of High-Resolution Remote Sensing Image[J]. APPLIED SCIENCES-BASEL,2019,9(15):20.
APA	Ding, Kaimeng,Yang, Zedong,Wang, Yingying,&Liu, Yueming.(2019).An Improved Perceptual Hash Algorithm Based on U-Net for the Authentication of High-Resolution Remote Sensing Image.APPLIED SCIENCES-BASEL,9(15),20.
MLA	Ding, Kaimeng,et al."An Improved Perceptual Hash Algorithm Based on U-Net for the Authentication of High-Resolution Remote Sensing Image".APPLIED SCIENCES-BASEL 9.15(2019):20.