{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T12:56:19Z","timestamp":1771851379724,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T00:00:00Z","timestamp":1655424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"China\u2019s National Natural Science Foundation","award":["41801303"],"award-info":[{"award-number":["41801303"]}]},{"name":"China\u2019s National Natural Science Foundation","award":["61902163"],"award-info":[{"award-number":["61902163"]}]},{"name":"China\u2019s National Natural Science Foundation","award":["42101430"],"award-info":[{"award-number":["42101430"]}]},{"name":"Nanjing City Sci-Tech Innovation Team of Smart Transportation and Vehicle-Road Collaboration","award":["41801303"],"award-info":[{"award-number":["41801303"]}]},{"name":"Nanjing City Sci-Tech Innovation Team of Smart Transportation and Vehicle-Road Collaboration","award":["61902163"],"award-info":[{"award-number":["61902163"]}]},{"name":"Nanjing City Sci-Tech Innovation Team of Smart Transportation and Vehicle-Road Collaboration","award":["42101430"],"award-info":[{"award-number":["42101430"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/a15060213","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T05:25:11Z","timestamp":1655443511000},"page":"213","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A New Subject-Sensitive Hashing Algorithm Based on MultiRes-RCF for Blockchains of HRRS Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1339-813X","authenticated-orcid":false,"given":"Kaimeng","family":"Ding","sequence":"first","affiliation":[{"name":"Jinling Institute of Technology, Nanjing 211169, China"},{"name":"Jiangsu AI Transportation Innovations & Applications Engineering Research Center, Nanjing 211169, China"}]},{"given":"Shiping","family":"Chen","sequence":"additional","affiliation":[{"name":"CSIRO Data61, Sydney, NSW 1710, Australia"}]},{"given":"Jiming","family":"Yu","sequence":"additional","affiliation":[{"name":"Jinling Institute of Technology, Nanjing 211169, China"}]},{"given":"Yanan","family":"Liu","sequence":"additional","affiliation":[{"name":"Jinling Institute of Technology, Nanjing 211169, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1745-3319","authenticated-orcid":false,"given":"Jie","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112381","DOI":"10.1016\/j.rse.2021.112381","article-title":"A review of geostatistical simulation models applied to satellite remote sensing: Methods and appli-cations","volume":"259","author":"Zakeri","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2357","DOI":"10.1109\/JSTARS.2022.3157648","article-title":"A Siamese Network Based U-Net for Change Detection in High Resolution Remote Sensing Images","volume":"15","author":"Chen","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"106033","DOI":"10.1016\/j.compag.2021.106033","article-title":"A comprehensive review on recent applications of unmanned aerial vehicle remote sensing with various sensors for high-throughput plant phenotyping","volume":"182","author":"Feng","year":"2021","journal-title":"Comput. Electron."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"30583","DOI":"10.1007\/s11042-020-08802-z","article-title":"Fingerprint-related chaotic image encryption scheme based on blockchain framework","volume":"80","author":"Li","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"101812","DOI":"10.1016\/j.compmedimag.2020.101812","article-title":"An Integration of blockchain and AI for secure data sharing and detection of CT images for the hospitals","volume":"87","author":"Kumar","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"100066","DOI":"10.1016\/j.bcra.2022.100066","article-title":"DHT- and blockchain-based smart identification for video conferencing","volume":"3","author":"Alizadeh","year":"2022","journal-title":"Blockchain Res. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, L., Gao, Y.H., Chen, J.Y., Wang, X.H., Huang, Z.Y., and Wei, D. (2019, January 9\u201311). Research on remote sensing data sharing model based on blockchain technology. Proceedings of the 2019 2nd International Conference on Blockchain Technology and Applications (ICBTA 2019), Xi\u2019an, China.","DOI":"10.1145\/3376044.3376047"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Pincheira, M., Donini, E., Giaffreda, R., and Vecchio, M. (2020, January 22\u201326). A Blockchain-Based Approach to Enable Remote Sensing Trusted Data. Proceedings of the 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS), Santiago, Chile.","DOI":"10.1109\/LAGIRS48042.2020.9165589"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ding, K., Liu, Y., Xu, Q., and Lu, F. (2020). A Subject-Sensitive Perceptual Hash Based on MUM-Net for the Integrity Authentication of High Resolution Remote Sensing Images. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9080485"},{"key":"ref_10","first-page":"1405","article-title":"An Overview of Perceptual Hashing","volume":"36","author":"Niu","year":"2008","journal-title":"Acta Electron. Sin."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yang, H., Yin, J., and Jiang, M. (2018). Perceptual Image Hashing Using Latent Low-Rank Representation and Uniform LBP. Appl. Sci., 8.","DOI":"10.3390\/app8020317"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"115713","DOI":"10.1016\/j.image.2019.115713","article-title":"Perceptual hashing for image authentication: A survey","volume":"81","author":"Du","year":"2020","journal-title":"Signal Process. Image Commun."},{"key":"ref_13","unstructured":"Nakamoto, S. (2021, April 10). A Peer-to-Peer Electronic Cash System. Available online: https:\/\/bitcoin.org\/bitcoin.pdf."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhai, P., He, J., and Zhu, N. (2022). Blockchain-Based Internet of Things Access Control Technology in Intelligent Manufacturing. Appl. Sci., 12.","DOI":"10.3390\/app12073692"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Meidute-Kavaliauskiene, I., Yazdi, A.K., and Mehdiabadi, A. (2022). Integration of Blockchain Technology and Prioritization of Deploy-ment Barriers in the Blood Supply Chain. Logistics, 6.","DOI":"10.3390\/logistics6010021"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, Z.-P., Ceong, H.-T., and Lee, S.-J. (2021). The Effect of Blockchain Operation Capabilities on Competitive Performance in Supply Chain Management. Sustainability, 13.","DOI":"10.3390\/su132112078"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Venkatraman, S., and Parvin, S. (2022). Developing an IoT Identity Management System Using Blockchain. Systems, 10.","DOI":"10.3390\/systems10020039"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Butt, G.Q., Sayed, T.A., Riaz, R., Rizvi, S.S., and Paul, A. (2022). Secure Healthcare Record Sharing Mechanism with Blockchain. Appl. Sci., 12.","DOI":"10.3390\/app12052307"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chivu, R.-G., Popa, I.-C., Orzan, M.-C., Marinescu, C., Florescu, M.S., and Orzan, A.-O. (2022). The Role of Blockchain Technologies in the Sustainable Development of Students\u2019 Learning Process. Sustainability, 14.","DOI":"10.3390\/su14031406"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Zhou, C., Guo, X., Song, Y., and Chen, C. (2022). A Novel Decentralized E-Commerce Transaction System Based on Blockchain. Appl. Sci., 12.","DOI":"10.3390\/app12125770"},{"key":"ref_21","first-page":"22","article-title":"B-DEC: Digital evidence cabinet based on blockchain for evidence management","volume":"181","author":"Yunianto","year":"2019","journal-title":"Int. J. Comput. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"60280","DOI":"10.1109\/ACCESS.2021.3074055","article-title":"Semi-U-Net: A Lightweight Deep Neural Network for Subject-Sensitive Hashing of HRRS Images","volume":"9","author":"Ding","year":"2021","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ding, K.M., Chen, S.B., Wang, Y., Liu, Y.M., Zeng, Y., and Tian, J. (2021). AAU-Net: Attention-Based Asymmetric U-Net for Sub-ject-Sensitive Hashing of Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13245109"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, J., Yang, D., and Hu, F. (2022). Multiscale Object Detection in Remote Sensing Images Combined with Multi-Receptive-Field Features and Relation-Connected Attention. Remote Sens., 14.","DOI":"10.3390\/rs14020427"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Yang, M., Wang, M., Qian, X., Yang, R., Zhang, X., and Dong, W. (2022). Semi-Supervised Adversarial Semantic Segmentation Network Using Transformer and Multiscale Convolution for High-Resolution Remote Sensing Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14081786"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1939","DOI":"10.1109\/TPAMI.2018.2878849","article-title":"Richer Convolutional Features for Edge Detection","volume":"41","author":"Liu","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","article-title":"MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation","volume":"121","author":"Ibtehaz","year":"2020","journal-title":"Neural Netw."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_29","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., and Kainz, B. (2018). Attention u-net: Learning where to look for the pancreas. arXiv."},{"key":"ref_30","first-page":"448","article-title":"Building extraction via convolutional neural networks from an open remote sensing building dataset","volume":"48","author":"Ji","year":"2019","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Xia, G.S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., and Zhang, L. (2018, January 18\u201322). DOTA: A large-scale dataset for object detection in aerial images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00418"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/6\/213\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:33:38Z","timestamp":1760139218000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/6\/213"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,17]]},"references-count":32,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["a15060213"],"URL":"https:\/\/doi.org\/10.3390\/a15060213","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,17]]}}}