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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Veröffentlicht in:Algorithms Jg. 15; H. 6; S. 213
Hauptverfasser: Ding, Kaimeng, Chen, Shiping, Yu, Jiming, Liu, Yanan, Zhu, Jie
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.06.2022
Schlagworte:
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
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:1999-4893
1999-4893
DOI:10.3390/a15060213