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

• Published in: Algorithms Vol. 15; no. 6; p. 213
• Main Authors: Ding, Kaimeng, Chen, Shiping, Yu, Jiming, Liu, Yanan, Zhu, Jie
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2022
• Subjects: 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
• DOI: 10.3390/a15060213

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.