{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T08:28:42Z","timestamp":1768724922685,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,5]],"date-time":"2023-02-05T00:00:00Z","timestamp":1675555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Major Key Project of PCL","award":["PCL2022A03"],"award-info":[{"award-number":["PCL2022A03"]}]},{"name":"Major Key Project of PCL","award":["2019B010136003"],"award-info":[{"award-number":["2019B010136003"]}]},{"name":"Major Key Project of PCL","award":["2020KCXTD007"],"award-info":[{"award-number":["2020KCXTD007"]}]},{"name":"Major Key Project of PCL","award":["202032854"],"award-info":[{"award-number":["202032854"]}]},{"name":"Major Key Project of PCL","award":["62250410365"],"award-info":[{"award-number":["62250410365"]}]},{"name":"Major Key Project of PCL","award":["202201010606"],"award-info":[{"award-number":["202201010606"]}]},{"name":"Guangdong Key R&amp;D Program of China","award":["PCL2022A03"],"award-info":[{"award-number":["PCL2022A03"]}]},{"name":"Guangdong Key R&amp;D Program of China","award":["2019B010136003"],"award-info":[{"award-number":["2019B010136003"]}]},{"name":"Guangdong Key R&amp;D Program of China","award":["2020KCXTD007"],"award-info":[{"award-number":["2020KCXTD007"]}]},{"name":"Guangdong Key R&amp;D Program of China","award":["202032854"],"award-info":[{"award-number":["202032854"]}]},{"name":"Guangdong Key R&amp;D Program of China","award":["62250410365"],"award-info":[{"award-number":["62250410365"]}]},{"name":"Guangdong Key R&amp;D Program of China","award":["202201010606"],"award-info":[{"award-number":["202201010606"]}]},{"name":"Guangdong Higher Education Innovation Group","award":["PCL2022A03"],"award-info":[{"award-number":["PCL2022A03"]}]},{"name":"Guangdong Higher Education Innovation Group","award":["2019B010136003"],"award-info":[{"award-number":["2019B010136003"]}]},{"name":"Guangdong Higher Education Innovation Group","award":["2020KCXTD007"],"award-info":[{"award-number":["2020KCXTD007"]}]},{"name":"Guangdong Higher Education Innovation Group","award":["202032854"],"award-info":[{"award-number":["202032854"]}]},{"name":"Guangdong Higher Education Innovation Group","award":["62250410365"],"award-info":[{"award-number":["62250410365"]}]},{"name":"Guangdong Higher Education Innovation Group","award":["202201010606"],"award-info":[{"award-number":["202201010606"]}]},{"name":"Guangzhou Higher Education Innovation Group","award":["PCL2022A03"],"award-info":[{"award-number":["PCL2022A03"]}]},{"name":"Guangzhou Higher Education Innovation Group","award":["2019B010136003"],"award-info":[{"award-number":["2019B010136003"]}]},{"name":"Guangzhou Higher Education Innovation Group","award":["2020KCXTD007"],"award-info":[{"award-number":["2020KCXTD007"]}]},{"name":"Guangzhou Higher Education Innovation Group","award":["202032854"],"award-info":[{"award-number":["202032854"]}]},{"name":"Guangzhou Higher Education Innovation Group","award":["62250410365"],"award-info":[{"award-number":["62250410365"]}]},{"name":"Guangzhou Higher Education Innovation Group","award":["202201010606"],"award-info":[{"award-number":["202201010606"]}]},{"name":"National Natural Science Foundation of China","award":["PCL2022A03"],"award-info":[{"award-number":["PCL2022A03"]}]},{"name":"National Natural Science Foundation of China","award":["2019B010136003"],"award-info":[{"award-number":["2019B010136003"]}]},{"name":"National Natural Science Foundation of China","award":["2020KCXTD007"],"award-info":[{"award-number":["2020KCXTD007"]}]},{"name":"National Natural Science Foundation of China","award":["202032854"],"award-info":[{"award-number":["202032854"]}]},{"name":"National Natural Science Foundation of China","award":["62250410365"],"award-info":[{"award-number":["62250410365"]}]},{"name":"National Natural Science Foundation of China","award":["202201010606"],"award-info":[{"award-number":["202201010606"]}]},{"name":"Guangzhou Science and Technology Program of China","award":["PCL2022A03"],"award-info":[{"award-number":["PCL2022A03"]}]},{"name":"Guangzhou Science and Technology Program of China","award":["2019B010136003"],"award-info":[{"award-number":["2019B010136003"]}]},{"name":"Guangzhou Science and Technology Program of China","award":["2020KCXTD007"],"award-info":[{"award-number":["2020KCXTD007"]}]},{"name":"Guangzhou Science and Technology Program of China","award":["202032854"],"award-info":[{"award-number":["202032854"]}]},{"name":"Guangzhou Science and Technology Program of China","award":["62250410365"],"award-info":[{"award-number":["62250410365"]}]},{"name":"Guangzhou Science and Technology Program of China","award":["202201010606"],"award-info":[{"award-number":["202201010606"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep neural networks (DNNs) can improve the image analysis and interpretation of remote sensing technology by extracting valuable information from images, and has extensive applications such as military affairs, agriculture, environment, transportation, and urban division. The DNNs for object detection can identify and analyze objects in remote sensing images through fruitful features of images, which improves the efficiency of image processing and enables the recognition of large-scale remote sensing images. However, many studies have shown that deep neural networks are vulnerable to adversarial attack. After adding small perturbations, the generated adversarial examples will cause deep neural network to output undesired results, which will threaten the normal recognition and detection of remote sensing systems. According to the application scenarios, attacks can be divided into the digital domain and the physical domain, the digital domain attack is directly modified on the original image, which is mainly used to simulate the attack effect, while the physical domain attack adds perturbation to the actual objects and captures them with device, which is closer to the real situation. Attacks in the physical domain are more threatening, however, existing attack methods generally generate the patch with bright style and a large attack range, which is easy to be observed by human vision. Our goal is to generate a natural patch with a small perturbation area, which can help some remote sensing images used in the military to avoid detection by object detectors and im-perceptible to human eyes. To address the above issues, we generate a rust-style adversarial patch generation framework based on style transfer. The framework takes a heat map-based interpretability method to obtain key areas of target recognition and generate irregular-shaped natural-looking patches to reduce the disturbance area and alleviates suspicion from humans. To make the generated adversarial examples have a higher attack success rate in the physical domain, we further improve the robustness of the adversarial patch through data augmentation methods such as rotation, scaling, and brightness, and finally, make it impossible for the object detector to detect the camouflage patch. We have attacked the YOLOV3 detection network on multiple datasets. The experimental results show that our model has achieved a success rate of 95.7% in the digital domain. We also conduct physical attacks in indoor and outdoor environments and achieve an attack success rate of 70.6% and 65.3%, respectively. The structural similarity index metric shows that the adversarial patches generated are more natural than existing methods.<\/jats:p>","DOI":"10.3390\/rs15040885","type":"journal-article","created":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T05:29:05Z","timestamp":1675661345000},"page":"885","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Rust-Style Patch: A Physical and Naturalistic Camouflage Attacks on Object Detector for Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Binyue","family":"Deng","sequence":"first","affiliation":[{"name":"Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China"}]},{"given":"Denghui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China"}]},{"given":"Fashan","family":"Dong","sequence":"additional","affiliation":[{"name":"Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China"}]},{"given":"Junjian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China"}]},{"given":"Muhammad","family":"Shafiq","sequence":"additional","affiliation":[{"name":"Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China"}]},{"given":"Zhaoquan","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"},{"name":"Department of New Networks, Peng Cheng Laboratory, Shenzhen 518055, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Qian, R., Lai, X., and Li, X. (2022). 3D Object Detection for Autonomous Driving: A Survey. Pattern Recognit., 130.","DOI":"10.1016\/j.patcog.2022.108796"},{"key":"ref_2","unstructured":"Fang, W., Shen, L., and Chen, Y. (2021). Artificial Intelligence and Security, Springer International Publishing. Lecture Notes in Computer Science."},{"key":"ref_3","first-page":"3523","article-title":"Image Segmentation Using Deep Learning: A Survey","volume":"44","author":"Minaee","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s00500-022-07522-w","article-title":"Ship Detection Based on Deep Learning Using SAR Imagery: A Systematic Literature Review","volume":"27","author":"Yasir","year":"2022","journal-title":"Soft Comput."},{"key":"ref_5","unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R. (2014, January 14\u201316). Intriguing Properties of Neural Networks. Proceedings of the International Conference on Learning Representations (ICLR), Banff, AB, Canada."},{"key":"ref_6","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A. (May, January 30). Towards Deep Learning Models Resistant to Adversarial Attacks. Proceedings of the International Conference on Learning Representations (ICLR), Vancouver, BC, Canada."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1109\/TGCN.2021.3095707","article-title":"IEPSBP: A Cost-Efficient Image Encryption Algorithm Based on Parallel Chaotic System for Green IoT","volume":"6","author":"Gu","year":"2021","journal-title":"IEEE Trans. Green Commun. Netw."},{"key":"ref_8","unstructured":"Goodfellow, I.J., Shlens, J., and Szegedy, C. (2015). Explaining and Harnessing Adversarial Examples. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Van Etten, A. (2022). The Weaknesses of Adversarial Camouflage in Overhead Imagery. arXiv.","DOI":"10.1109\/AIPR57179.2022.10092201"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kurakin, A., Goodfellow, I., and Bengio, S. (2017). Adversarial Examples in the Physical World. arXiv.","DOI":"10.1201\/9781351251389-8"},{"key":"ref_11","unstructured":"Brown, T.B., Man\u00e9, D., Roy, A., Abadi, M., and Gilmer, J. (2018). Adversarial Patch. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dong, Y., Liao, F., Pang, T., Su, H., Zhu, J., Hu, X., and Li, J. (2018, January 18\u201323). Boosting Adversarial Attacks with Momentum. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00957"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Carlini, N., and Wagner, D. (2017, January 22\u201326). Towards Evaluating the Robustness of Neural Networks. Proceedings of the 2017 IEEE Symposium on Security and Privacy (SP), San Jose, CA, USA.","DOI":"10.1109\/SP.2017.49"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.neucom.2022.05.054","article-title":"Leveraging transferability and improved beam search in textual adversarial attacks","volume":"500","author":"Zhu","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.M., Fawzi, A., and Frossard, P. (2016, January 27\u201330). DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.282"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.M., Fawzi, A., Fawzi, O., and Frossard, P. (2017). Universal Adversarial Perturbations. arXiv.","DOI":"10.1109\/CVPR.2017.17"},{"key":"ref_17","unstructured":"Athalye, A., Engstrom, L., Ilyas, A., and Kwok, K. (2018, January 10\u201315). Synthesizing Robust Adversarial Examples. Proceedings of the 35th International Conference on Machine Learning (PMLR), Stockholm, Sweden."},{"key":"ref_18","unstructured":"Liu, X., Yang, H., Liu, Z., Song, L., Li, H., and Chen, Y. (2019). DPatch: An Adversarial Patch Attack on Object Detectors. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chow, K.H., Liu, L., Loper, M., Bae, J., Gursoy, M.E., Truex, S., Wei, W., and Wu, Y. (2020, January 28\u201331). Adversarial Objectness Gradient Attacks in Real-time Object Detection Systems. Proceedings of the 2020 Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), Atlanta, GA, USA.","DOI":"10.1109\/TPS-ISA50397.2020.00042"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"133049","DOI":"10.1109\/ACCESS.2021.3115764","article-title":"Robust Adversarial Attack Against Explainable Deep Classification Models Based on Adversarial Images with Different Patch Sizes and Perturbation Ratios","volume":"9","author":"Le","year":"2021","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Adhikari, A., Hollander, R.D., Tolios, I., Bekkum, M.V., and Raaijmakers, S. (2020). Adversarial Patch Camouflage against Aerial Detection. arXiv.","DOI":"10.1117\/12.2575907"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., and Song, D. (2018, January 18\u201323). Robust Physical-World Attacks on Deep Learning Visual Classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00175"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhou, W., and Li, H. (2020, January 6\u201310). Contextual Adversarial Attacks For Object Detection. Proceedings of the 2020 IEEE International Conference on Multimedia and Expo (ICME), London, UK.","DOI":"10.1109\/ICME46284.2020.9102805"},{"key":"ref_24","first-page":"52","article-title":"ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector","volume":"Volume 11051","author":"Chen","year":"2019","journal-title":"Machine Learning and Knowledge Discovery in Databases"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chattopadhay, A., Sarkar, A., Howlader, P., and Balasubramanian, V.N. (2018, January 12\u201315). Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00097"},{"key":"ref_27","unstructured":"Sitawarin, C., Bhagoji, A.N., Mosenia, A., Chiang, M., and Mittal, P. (2018). DARTS: Deceiving Autonomous Cars with Toxic Signs. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Duan, R., Mao, X., Qin, A.K., Chen, Y., Ye, S., He, Y., and Yang, Y. (2021, January 20\u201325). Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01580"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Gnanasambandam, A., Sherman, A.M., and Chan, S.H. (2021, January 11\u201317). Optical Adversarial Attack. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) Workshops, Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00016"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_31","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Thys, S., Van Ranst, W., and Goedeme, T. (2019, January 16\u201320). Fooling Automated Surveillance Cameras: Adversarial Patches to Attack Person Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00012"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/j.ins.2020.08.087","article-title":"Towards a Physical-World Adversarial Patch for Blinding Object Detection Models","volume":"556","author":"Wang","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_35","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hu, Y.C.T., Kung, B.H., Tan, D.S., Chen, J.C., Hua, K.L., and Cheng, W.H. (2021, January 10\u201317). Naturalistic Physical Adversarial Patch for Object Detectors. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00775"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Du, A., Chen, B., Chin, T.J., Law, Y.W., Sasdelli, M., Rajasegaran, R., and Campbell, D. (2022, January 4\u20138). Physical Adversarial Attacks on an Aerial Imagery Object Detector. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.","DOI":"10.1109\/WACV51458.2022.00385"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Xue, M., Yuan, C., He, C., Wang, J., and Liu, W. (2021). NaturalAE: Natural and Robust Physical Adversarial Examples for Object Detectors. J. Inf. Secur. Appl., 57.","DOI":"10.1016\/j.jisa.2020.102694"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (2016, January 27\u201330). Learning Deep Features for Discriminative Localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"921","DOI":"10.1109\/TNSE.2020.2996738","article-title":"Gradient Shielding: Towards Understanding Vulnerability of Deep Neural Networks","volume":"8","author":"Gu","year":"2021","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_41","first-page":"1028","article-title":"Perceptual-Sensitive GAN for Generating Adversarial Patches","volume":"33","author":"Liu","year":"2019","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_42","unstructured":"Subramanya, A., Pillai, V., and Pirsiavash, H. (November, January 27). Fooling Network Interpretation in Image Classification. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, South Korea."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wang, J., Liu, A., Yin, Z., Liu, S., Tang, S., and Liu, X. (2021, January 20\u201325). Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00846"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Gatys, L.A., Ecker, A.S., and Bethge, M. (2015). A Neural Algorithm of Artistic Style. arXiv.","DOI":"10.1167\/16.12.326"},{"key":"ref_45","unstructured":"Sharif, M., Bhagavatula, S., Bauer, L., and Reiter, M.K. (2016). CCS \u201916: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, Association for Computing Machinery."},{"key":"ref_46","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_47","unstructured":"Song, D., Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Tramer, F., Prakash, A., and Kohno, T. (2018, January 13\u201314). Physical Adversarial Examples for Object Detectors. Proceedings of the 12th USENIX Workshop on Offensive Technologies, Baltimore, MD, USA."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/885\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:25:03Z","timestamp":1760120703000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/885"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,5]]},"references-count":47,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15040885"],"URL":"https:\/\/doi.org\/10.3390\/rs15040885","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,5]]}}}