{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T06:26:35Z","timestamp":1761719195406,"version":"build-2065373602"},"reference-count":66,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,3,3]],"date-time":"2023-03-03T00:00:00Z","timestamp":1677801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Nature Science Foundation of Shaanxi","award":["2022JQ-653","D5000210767"],"award-info":[{"award-number":["2022JQ-653","D5000210767"]}]},{"name":"The Fundamental Research Funds for the Central Universities, Northwestern Polytechnical University","award":["2022JQ-653","D5000210767"],"award-info":[{"award-number":["2022JQ-653","D5000210767"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The \u201clow, slow, and small\u201d target (LSST) poses a significant threat to the military ground unit. It is hard to defend against due to its invisibility to numerous detecting devices. With the onboard deep learning-based object detection methods, the intelligent LSST (ILSST) can find and detect the ground unit autonomously in a denied environment. This paper proposes an adversarial patch-based defending method to blind the ILSST by attacking its onboard object detection network. First, an adversarial influence score was established to indicate the influence of the adversarial noise on the objects. Then, based on this score, we used the least squares algorithm and Bisectional search methods to search the patch\u2019s optimal coordinates and size. Using the optimal coordinates and size, an adaptive patch-generating network was constructed to automatically generate patches on ground units and hide the ground units from the deep learning-based object detection network. To evaluate the efficiency of our algorithm, a new LSST view dataset was collected, and extensive attacking experiments are carried out on this dataset. The results demonstrate that our algorithm can effectively attack the object detection networks, is better than state-of-the-art adversarial patch-generating algorithms in hiding the ground units from the object detection networks, and has high transferability among the object detection networks.<\/jats:p>","DOI":"10.3390\/rs15051439","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T01:35:30Z","timestamp":1678066530000},"page":"1439","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["An Adaptive Adversarial Patch-Generating Algorithm for Defending against the Intelligent Low, Slow, and Small Target"],"prefix":"10.3390","volume":"15","author":[{"given":"Erkenbieke","family":"Jia","sequence":"first","affiliation":[{"name":"Unmanned System Research Institute, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]},{"given":"Yuelei","family":"Xu","sequence":"additional","affiliation":[{"name":"Unmanned System Research Institute, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]},{"given":"Zhaoxiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Unmanned System Research Institute, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]},{"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Unmanned System Research Institute, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]},{"given":"Weijia","family":"Feng","sequence":"additional","affiliation":[{"name":"Unmanned System Research Institute, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]},{"given":"Liheng","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Electronics And Information, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]},{"given":"Tian","family":"Hui","sequence":"additional","affiliation":[{"name":"Unmanned System Research Institute, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]},{"given":"Chengyang","family":"Tao","sequence":"additional","affiliation":[{"name":"Unmanned System Research Institute, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,3]]},"reference":[{"key":"ref_1","first-page":"4146212","article-title":"Low-Altitude and Slow-Speed Small Target Detection Based on Spectrum Zoom Processing","volume":"2018","author":"Zhang","year":"2018","journal-title":"Math. Probl. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Valenti, F., Giaquinto, D., Musto, L., Zinelli, A., Bertozzi, M., and Broggi, A. (2018, January 4\u20137). Enabling computer vision-based autonomous navigation for Unmanned Aerial Vehicles in cluttered GPS-denied environments. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569695"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hameed, S., Junejo, F., Zai, M.A.Y., and Amin, I. (2018, January 22\u201323). Prediction of Civilians Killing in the Upcoming Drone Attack. Proceedings of the 2018 IEEE 5th International Conference on Engineering Technologies and Applied Sciences (ICETAS), Bangkok, Thailand.","DOI":"10.1109\/ICETAS.2018.8629174"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1049\/et.2021.0411","article-title":"Consumer drones used in bomb attacks: Terrorist rebels are turning consumer drones into deadly weapons. E&T investigates why it goes on and what can be done about it","volume":"16","author":"Heubl","year":"2021","journal-title":"Eng. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bj\u00f6rklund, S. (2018, January 26\u201328). Target detection and classification of small drones by boosting on radar micro-Doppler. Proceedings of the 2018 15th European Radar Conference (EuRAD), Madrid, Spain.","DOI":"10.23919\/EuRAD.2018.8546569"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Semkin, V., Yin, M., Hu, Y., Mezzavilla, M., and Rangan, S. (2020, January 25\u201328). Drone detection and classification based on radar cross section signatures. Proceedings of the 2020 International Symposium on Antennas and Propagation (ISAP), Osaka, Japan.","DOI":"10.23919\/ISAP47053.2021.9391260"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Al-Emadi, S., Al-Ali, A., and Al-Ali, A. (2021). Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks. Sensors, 21.","DOI":"10.3390\/s21154953"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4570","DOI":"10.1109\/JSEN.2018.2825879","article-title":"Acoustic sensing from a multi-rotor drone","volume":"18","author":"Wang","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sie, N.J., Srigrarom, S., and Huang, S. (2021, January 16\u201318). Field test validations of vision-based multi-camera multi-drone tracking and 3D localizing with concurrent camera pose estimation. Proceedings of the 2021 6th International Conference on Control and Robotics Engineering (ICCRE), Beijing, China.","DOI":"10.1109\/ICCRE51898.2021.9435654"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Singha, S., and Aydin, B. (2021). Automated Drone Detection Using YOLOv4. Drones, 5.","DOI":"10.3390\/drones5030095"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ezuma, M., Erden, F., Anjinappa, C.K., Ozdemir, O., and Guvenc, I. (2019, January 2\u20139). Micro-UAV detection and classification from RF fingerprints using machine learning techniques. Proceedings of the 2019 IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2019.8741970"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5150","DOI":"10.1109\/JSEN.2021.3105229","article-title":"UAV Detection and Localization Based on Multi-dimensional Signal Features","volume":"22","author":"Nie","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hoffmann, F., Ritchie, M., Fioranelli, F., Charlish, A., and Griffiths, H. (2016, January 2\u20136). Micro-Doppler based detection and tracking of UAVs with multistatic radar. Proceedings of the 2016 IEEE radar conference (RadarConf), Philadelphia, PA, USA.","DOI":"10.1109\/RADAR.2016.7485236"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"10121","DOI":"10.1109\/JSEN.2019.2927370","article-title":"A software platform for noise reduction in sound sensor equipped drones","volume":"19","author":"Kang","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Cao, Y., Ding, M., Zhuang, L., and Yao, W. (2016, January 7\u201310). An intruder detection algorithm for vision based sense and avoid system. Proceedings of the 2016 International Conference on Unmanned Aircraft Systems (ICUAS), Arlington, VA, USA.","DOI":"10.1109\/ICUAS.2016.7502521"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ganti, S.R., and Kim, Y. (2016, January 7\u201310). Implementation of detection and tracking mechanism for small UAS. Proceedings of the 2016 International Conference on Unmanned Aircraft Systems (ICUAS), Arlington, VA, USA.","DOI":"10.1109\/ICUAS.2016.7502513"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"114574","DOI":"10.1109\/ACCESS.2021.3104738","article-title":"Drone Detection Sensor with Continuous 2.4 GHz ISM Band Coverage Based on Cost-Effective SDR Platform","volume":"9","author":"Flak","year":"2021","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"12150","DOI":"10.1109\/TVT.2019.2949345","article-title":"Impact of an interfering node on unmanned aerial vehicle communications","volume":"68","author":"Kim","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_19","unstructured":"Shepard, D.P., Bhatti, J.A., and Humphreys, T.E. (2022, November 03). Drone hack: Spoofing attack demonstration on a civilian unmanned aerial vehicle. Available online: https:\/\/www.gpsworld.com\/drone-hack\/."},{"key":"ref_20","unstructured":"Glenn, J. (2022, June 12). Ultralytics\/Yolov5: V6.0. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xu, Y., Bai, T., Yu, W., Chang, S., Atkinson, P.M., and Ghamisi, P. (2022). AI Security for Geoscience and Remote Sensing: Challenges and Future Trends. arXiv.","DOI":"10.1109\/MGRS.2023.3272825"},{"key":"ref_22","unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R. (2013). Intriguing properties of neural networks. arXiv."},{"key":"ref_23","unstructured":"Goodfellow, I.J., Shlens, J., and Szegedy, C. (2014). Explaining and harnessing adversarial examples. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kurakin, A., Goodfellow, I.J., and Bengio, S. (2018). Adversarial examples in the physical world. Artificial Intelligence Safety and Security, Chapman and Hall\/CRC.","DOI":"10.1201\/9781351251389-8"},{"key":"ref_25","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, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00957"},{"key":"ref_26","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, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.282"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., and Swami, A. (2019, January 21\u201324). The limitations of deep learning in adversarial settings. Proceedings of the 2016 IEEE European Symposium on Security and Privacy (EuroS&P), Saarbrucken, Germany.","DOI":"10.1109\/EuroSP.2016.36"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Carlini, N., and Wagner, D. (2017, January 22\u201324). 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_29","doi-asserted-by":"crossref","unstructured":"Rony, J., Hafemann, L.G., Oliveira, L.S., Ayed, I.B., Sabourin, R., and Granger, E. (2019, January 15\u201320). Decoupling direction and norm for efficient gradient-based l2 adversarial attacks and defenses. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00445"},{"key":"ref_30","unstructured":"Croce, F., and Hein, M. (2020, January 13\u201318). Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. Proceedings of the International Conference on Machine Learning, Virtual."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Xiao, C., Li, B., Zhu, J.-Y., He, W., Liu, M., and Song, D. (2018). Generating adversarial examples with adversarial networks. arXiv.","DOI":"10.24963\/ijcai.2018\/543"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Bai, T., Zhao, J., Zhu, J., Han, S., Chen, J., Li, B., and Kot, A. (2021, January 19\u201322). Ai-gan: Attack-inspired generation of adversarial examples. Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA.","DOI":"10.1109\/ICIP42928.2021.9506278"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xie, C., Wang, J., Zhang, Z., Zhou, Y., Xie, L., and Yuille, A. (2017, January 22\u201329). Adversarial Examples for Semantic Segmentation and Object Detection. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.153"},{"key":"ref_34","unstructured":"Li, Y., Tian, D., Chang, M.-C., Bian, X., and Lyu, S. (2018). Robust adversarial perturbation on deep proposal-based models. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wei, X., Liang, S., Chen, N., and Cao, X. (2018). Transferable adversarial attacks for image and video object detection. arXiv.","DOI":"10.24963\/ijcai.2019\/134"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_37","unstructured":"Athalye, A., Engstrom, L., Ilyas, A., and Kwok, K. (2018, January 10\u201315). Synthesizing Robust Adversarial Examples. Proceedings of the International Conference on Machine Learning, Stockholm, Sweden."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"7427","DOI":"10.1109\/TCYB.2020.3041481","article-title":"Daedalus: Breaking Nonmaximum Suppression in Object Detection via Adversarial Examples","volume":"52","author":"Wang","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_39","unstructured":"Brown, T.B., Man\u00e9, D., Roy, A., Abadi, M., and Gilmer, J. (2017). Adversarial patch. arXiv."},{"key":"ref_40","unstructured":"Karmon, D., Zoran, D., and Goldberg, Y. (2018, January 10\u201315). Lavan: Localized and visible adversarial noise. Proceedings of the International Conference on Machine Learning, Stockholm, Sweden."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chindaudom, A., Siritanawan, P., Sumongkayothin, K., and Kotani, K. (2020, January 26\u201329). AdversarialQR: An adversarial patch in QR code format. Proceedings of the 2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Kitakyushu, Japan.","DOI":"10.1109\/ICIEVicIVPR48672.2020.9306675"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chindaudom, A., Siritanawan, P., Sumongkayothin, K., and Kotani, K. (2022). Surreptitious Adversarial Examples through Functioning QR Code. J. Imaging, 8.","DOI":"10.3390\/jimaging8050122"},{"key":"ref_43","unstructured":"Gittings, T., Schneider, S., and Collomosse, J. (2019). Robust synthesis of adversarial visual examples using a deep image prior. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00138-021-01194-6","article-title":"A data independent approach to generate adversarial patches","volume":"32","author":"Zhou","year":"2021","journal-title":"Mach. Vis. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"9515","DOI":"10.1109\/JIOT.2021.3124815","article-title":"Inconspicuous adversarial patches for fooling image-recognition systems on mobile devices","volume":"9","author":"Bai","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_46","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 (WOOT 18), Baltimore, MD, USA."},{"key":"ref_47","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, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00175"},{"key":"ref_48","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. arXiv."},{"key":"ref_49","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_50","unstructured":"Wu, S., Dai, T., and Xia, S.-T. (2020). Dpattack: Diffused patch attacks against universal object detection. arXiv."},{"key":"ref_51","unstructured":"Lee, M., and Kolter, Z. (2019). On physical adversarial patches for object detection. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Thys, S., Van Ranst, W., and Goedem\u00e9, T. (2019, January 16\u201317). Fooling automated surveillance cameras: Adversarial patches to attack person detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00012"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Komkov, S., and Petiushko, A. (2021, January 10\u201315). AdvHat: Real-World Adversarial Attack on ArcFace Face ID System. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412236"},{"key":"ref_54","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_55","doi-asserted-by":"crossref","unstructured":"Czaja, W., Fendley, N., Pekala, M., Ratto, C., and Wang, I.-J. (2019, January 6\u20139). Adversarial examples in remote sensing. Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, DC, USA.","DOI":"10.1145\/3274895.3274904"},{"key":"ref_56","unstructured":"Chen, L., Zhu, G., Li, Q., and Li, H. (2019). Adversarial example in remote sensing image recognition. arXiv."},{"key":"ref_57","first-page":"1","article-title":"Universal adversarial examples in remote sensing: Methodology and benchmark","volume":"60","author":"Xu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, Y., Qi, J., Bin, K., Wen, H., Tong, X., and Zhong, P. (2022). Adversarial Patch Attack on Multi-Scale Object Detection for UAV Remote Sensing Images. Remote Sens., 14.","DOI":"10.20944\/preprints202210.0131.v1"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Bai, T., Wang, H., and Wen, B. (2022). Targeted Universal Adversarial Examples for Remote Sensing. Remote Sens., 14.","DOI":"10.3390\/rs14225833"},{"key":"ref_60","first-page":"1","article-title":"Hyperspectral image classification with adversarial attack","volume":"19","author":"Shi","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"7419","DOI":"10.1109\/TGRS.2021.3051641","article-title":"An empirical study of adversarial examples on remote sensing image scene classification","volume":"59","author":"Chen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1109\/JSTARS.2020.3038683","article-title":"Adversarial examples for CNN-based SAR image classification: An experience study","volume":"14","author":"Li","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2020, January 7\u201312). Distance-IoU loss: Faster and better learning for bounding box regression. Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, New York, New York, USA.","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"ref_64","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_65","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). Yolox: Exceeding yolo series in 2021. arXiv."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Sharif, M., Bhagavatula, S., Bauer, L., and Reiter, M.K. (2016, January 24\u201328). Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition. Proceedings of the 2016 Acm Sigsac Conference on Computer and Communications Security, Vienna, Austria.","DOI":"10.1145\/2976749.2978392"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1439\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:47:28Z","timestamp":1760122048000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1439"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,3]]},"references-count":66,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15051439"],"URL":"https:\/\/doi.org\/10.3390\/rs15051439","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,3,3]]}}}