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

文献类型：期刊论文

作者Ding, Kaimeng2,3; Yang, Zedong1; Wang, Yingying2; Liu, Yueming3
刊名APPLIED SCIENCES-BASEL
出版日期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
DOI10.3390/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.
WOS关键词DIGITAL SIGNATURE ; TRANSFORM
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研究方向Chemistry ; Materials Science ; Physics
语种英语
WOS记录号WOS:000482134500027
出版者MDPI
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
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.Chinese Acad Surveying & Mapping, Beijing 100039, Peoples R China
2.Jinling Inst Technol, Nanjing 211169, Jiangsu, 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.

入库方式： OAI收割

来源：地理科学与资源研究所

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