A New Subject-Sensitive Hashing Algorithm Based on MultiRes-RCF for Blockchains of HRRS Images

Aiming at the deficiency that blockchain technology is too sensitive to the binary-level changes of high resolution remote sensing (HRRS) images, we propose a new subject-sensitive hashing algorithm specially for HRRS image blockchains. To implement this subject-sensitive hashing algorithm, we desig...

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Vydané v:	Algorithms Ročník 15; číslo 6; s. 213
Hlavní autori:	Ding, Kaimeng, Chen, Shiping, Yu, Jiming, Liu, Yanan, Zhu, Jie
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.06.2022
Predmet:	Algorithms Analysis Artificial neural networks Blockchain blockchain for HRRS image Cryptography Feature extraction Hash based algorithms Image resolution integrity authentication Machine learning Neural networks Peer to peer computing perceptual hashing Remote sensing subject-sensitive hashing
ISSN:	1999-4893, 1999-4893
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Abstract	Aiming at the deficiency that blockchain technology is too sensitive to the binary-level changes of high resolution remote sensing (HRRS) images, we propose a new subject-sensitive hashing algorithm specially for HRRS image blockchains. To implement this subject-sensitive hashing algorithm, we designed and implemented a deep neural network model MultiRes-RCF (richer convolutional features) for extracting features from HRRS images. A MultiRes-RCF network is an improved RCF network that borrows the MultiRes mechanism of MultiResU-Net. The subject-sensitive hashing algorithm based on MultiRes-RCF can detect the subtle tampering of HRRS images while maintaining robustness to operations that do not change the content of the HRRS images. Experimental results show that our MultiRes-RCF-based subject-sensitive hashing algorithm has better tamper sensitivity than the existing deep learning models such as RCF, AAU-net, and Attention U-net, meeting the needs of HRRS image blockchains.
AbstractList	Aiming at the deficiency that blockchain technology is too sensitive to the binary-level changes of high resolution remote sensing (HRRS) images, we propose a new subject-sensitive hashing algorithm specially for HRRS image blockchains. To implement this subject-sensitive hashing algorithm, we designed and implemented a deep neural network model MultiRes-RCF (richer convolutional features) for extracting features from HRRS images. A MultiRes-RCF network is an improved RCF network that borrows the MultiRes mechanism of MultiResU-Net. The subject-sensitive hashing algorithm based on MultiRes-RCF can detect the subtle tampering of HRRS images while maintaining robustness to operations that do not change the content of the HRRS images. Experimental results show that our MultiRes-RCF-based subject-sensitive hashing algorithm has better tamper sensitivity than the existing deep learning models such as RCF, AAU-net, and Attention U-net, meeting the needs of HRRS image blockchains.
Audience	Academic
Author	Ding, Kaimeng Chen, Shiping Liu, Yanan Yu, Jiming Zhu, Jie
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SubjectTerms	Algorithms Analysis Artificial neural networks Blockchain blockchain for HRRS image Cryptography Feature extraction Hash based algorithms Image resolution integrity authentication Machine learning Neural networks Peer to peer computing perceptual hashing Remote sensing subject-sensitive hashing
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Title	A New Subject-Sensitive Hashing Algorithm Based on MultiRes-RCF for Blockchains of HRRS Images
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