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In this field, the adversarial examples (AEs) pose serious security risks and vulnerabilities to deep learning-based systems. Due to the limitation of object size, image degradation, and scene brightness, adding adversarial disturbances to small and dense objects is extremely challenging. To study the threat of AE for UAV object detection, a dynamic bi-level integrated attack (DBI-Attack) is proposed for intensive multi-scale UAV object detection. Firstly, we use the dynamic iterative attack (DIA) method to generate perturbation on the classification level by improving the momentum iterative fast gradient sign method (MIM). Secondly, the bi-level adversarial attack method (BAAM) is constructed to add global perturbation on the decision level for completing the white-box attack. Finally, the integrated black-box attack method (IBAM) is combined to realize the black-box mislabeling and fabrication attacks. We experiment on the real drone traffic vehicle detection datasets to better evaluate the attack effectiveness. The experimental results show that the proposed method can achieve mislabeling and fabrication attacks on the UAV object detectors in black-box conditions. Furthermore, the adversarial training is applied to improve the model robustness. This work aims to call more attention to the adversarial and defensive aspects of UAV target detection models.<\/jats:p>","DOI":"10.3390\/rs16142570","type":"journal-article","created":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T12:44:36Z","timestamp":1721047476000},"page":"2570","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["DBI-Attack:Dynamic Bi-Level Integrated Attack for Intensive Multi-Scale UAV Object Detection"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2667-1358","authenticated-orcid":false,"given":"Zhengyang","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Information and Navigation, Air Force Engineering University, Xi\u2019an 710082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5097-2922","authenticated-orcid":false,"given":"Buhong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Navigation, Air Force Engineering University, Xi\u2019an 710082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5765-0827","authenticated-orcid":false,"given":"Zhen","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710129, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8549-2096","authenticated-orcid":false,"given":"Xuan","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The University of Manchester, Manchester M13 9PL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10894","DOI":"10.1109\/TVT.2023.3262129","article-title":"Cross-Modal Object Detection Via UAV","volume":"72","author":"Li","year":"2023","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3007","DOI":"10.1109\/TGRS.2019.2946751","article-title":"Adversarial Robustness Enhancement of UAV-Oriented Automatic Image Recognition Based on Deep Ensemble Models","volume":"21","author":"Lu","year":"2020","journal-title":"Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lu, Z., Sun, H., Ji, K., and Kuang, G. (2023). Adversarial Robust Aerial Image Recognition Based on Reactive-Proactive Defense Framework with Deep Ensembles. Remote Sens., 15.","DOI":"10.3390\/rs15194660"},{"key":"ref_4","first-page":"493","article-title":"Real-Time Traffic End-of-Queue Detection and Tracking in UAV Video","volume":"21","author":"Messenger","year":"2023","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"12272","DOI":"10.1109\/TITS.2023.3290827","article-title":"Developing a More Reliable Framework for Extracting Traffic Data from a UAV Video","volume":"24","author":"Li","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3325","DOI":"10.1007\/s13042-020-01242-z","article-title":"Adversarial examples: Attacks and defenses in the physical world","volume":"12","author":"Ren","year":"2021","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_7","first-page":"2711","article-title":"Adversarial Sticker: A Stealthy Attack Method in the Physical World","volume":"45","author":"Wei","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2023.3335418","article-title":"Threatening patch attacks on object detection in optical remote sensing images","volume":"61","author":"Sun","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"12320","DOI":"10.1109\/JSEN.2023.3266653","article-title":"Adaptive image dehazing and object tracking in UAV videos based on the template updating siamese network","volume":"23","author":"Sun","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xu, Y., Sun, H., Chen, J., Lei, L., Kuang, G., and Ji, K. (2021, January 11\u201316). Robust remote sensing scene classification by adversarial self-supervised learning. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553824"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Xu, Y., Sun, H., Chen, J., Lei, L., Ji, K., and Kuang, G. (2021). Adversarial Self-Supervised Learning for Robust SAR Target Recognition. Remote Sens., 13.","DOI":"10.3390\/rs13204158"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1109\/TBDATA.2023.3296936","article-title":"A Black-Box Adversarial Attack Method via Nesterov Accelerated Gradient and Rewiring Towards Attacking Graph Neural Networks","volume":"9","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Big Data"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3433000","article-title":"Adversarial attacks and defenses in deep learning: From a perspective of cybersecurity","volume":"55","author":"Zhou","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cai, Z., Tan, Y., and Asif, M.S. (2023, January 18\u201322). Ensemble-based blackbox attacks on dense prediction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00394"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"109466","DOI":"10.1016\/j.patcog.2023.109466","article-title":"Adversarial pan-sharpening attacks for object detection in remote sensing","volume":"139","author":"Wei","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"22399","DOI":"10.1109\/JIOT.2021.3111024","article-title":"Adversarial attacks and defenses for deep-learning-based unmanned aerial vehicles","volume":"9","author":"Tian","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.neucom.2019.11.051","article-title":"Adversarial attacks on faster RCNN object detector","volume":"382","author":"Wang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_18","first-page":"110066","article-title":"Sequential architecture-agnostic black-box attack design and analysis","volume":"15","author":"Mumcu","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tian, J., Shen, C., Wang, B., Xia, X., Zhang, M., Lin, C., and Li, Q. (2024). LESSON: Multi-Label Adversarial False Data Injection Attack for Deep Learning Locational Detection. IEEE Transactions on Dependable and Secure Computing, IEEE.","DOI":"10.1109\/TDSC.2024.3353302"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1016\/j.ins.2020.05.089","article-title":"A discrete cosine transform-based query efficient attack on black-box object detectors","volume":"546","author":"Kuang","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2193461","DOI":"10.1080\/08839514.2023.2193461","article-title":"Towards autonomous driving model resistant to adversarial attack","volume":"37","author":"Shibly","year":"2023","journal-title":"Appl. Artif. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"103057","DOI":"10.1016\/j.cose.2022.103057","article-title":"LIGAA: Generative adversarial attack method based on low-frequency information","volume":"125","author":"Zhu","year":"2023","journal-title":"Comput. Secur."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2761","DOI":"10.1007\/s11063-022-10983-7","article-title":"Attacking object detector by simultaneously learning perturbations and locations","volume":"55","author":"Wang","year":"2023","journal-title":"Neural Process. Lett."},{"key":"ref_24","first-page":"112","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_25","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 21\u201326). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). SSD: Single shot multibox detector. Computer Vision ECCV 2016: 14th European Conference, Proceedings, Part I, Amsterdam, The Netherlands, 11\u201314 October 2016, Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sun, P., Zhang, R., Jiang, Y., Kong, T., Xu, C., Zhan, W., Tomizuka, M., Li, L., Yuan, Z., and Wang, C. (2021, January 20\u201325). Sparse R-CNN: End-to-end object detection with learnable proposals. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01422"},{"key":"ref_29","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A. (2017). Towards deep learning models resistant to adversarial attacks. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Xie, C., Wang, J., Zhang, Z., Zhou, Y., Xie, L., and Yuille, A. (2017, January 21\u201326). Adversarial examples for semantic segmentation and object detection. Proceedings of the IEEE International Conference on Computer Vision (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/ICCV.2017.153"},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"Du, M., Bi, D., Du, M., Xu, X., and Wu, Z. (2022). ULAN: A universal local adversarial network for SAR target recognition based on layer-wise relevance propagation. Remote Sens., 15.","DOI":"10.20944\/preprints202211.0243.v1"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TIP.2023.3345136","article-title":"Improving Transferability of Universal Adversarial Perturbation with Feature Disruption","volume":"33","author":"Wang","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"ref_34","unstructured":"Li, Y., Tian, D., Chang, M., Bian, X., and Lyu, S. (2018). Robust adversarial perturbation on deep proposal-based models. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wu, H., Rowlands, S., and Wahlstrom, J. (2024). A Man-in-the-Middle Attack against Object Detection Systems. arXiv.","DOI":"10.1109\/TAI.2024.3428520"},{"key":"ref_36","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_37","unstructured":"Zhang, X., Sun, C., and Han, H. (2022). Object-fabrication Targeted Attack for Object Detection. arXiv."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"102634","DOI":"10.1016\/j.jnca.2020.102634","article-title":"An adversarial attack on DNN-based black-box object detectors","volume":"161","author":"Wang","year":"2020","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"6337","DOI":"10.1109\/JIOT.2020.3016145","article-title":"Adaptive square attack: Fooling autonomous cars with adversarial traffic signs","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1109\/JETCAS.2021.3076101","article-title":"A new lightweight in situ adversarial sample detector for edge deep neural network","volume":"11","author":"Wang","year":"2021","journal-title":"IEEE J. Emerg. Sel. Top. Circuits Syst."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Yin, M., Li, S., Cai, Z., Song, C., Asif, M.S., Roy-Chowdhury, A.K., and Krishnamurthy, S.V. (2021, January 20\u201325). Exploiting multi-object relationships for detecting adversarial attacks in complex scenes. Proceedings of the IEEE\/CVF International Conference on Computer Vision (CVPR), Nashville, TN, USA.","DOI":"10.1109\/ICCV48922.2021.00776"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5743","DOI":"10.1109\/TNNLS.2021.3130991","article-title":"Low Complexity Gradient Computation Techniques to Accelerate Deep Neural Network Training","volume":"34","author":"Shin","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4507","DOI":"10.1109\/JSTARS.2022.3179171","article-title":"Adversarial deception against SAR target recognition network","volume":"15","author":"Zhang","year":"2022","journal-title":"IEEE J. Select.Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2164","DOI":"10.1109\/TNNLS.2019.2929059","article-title":"Real-time object detection with reduced region proposal network via multi-feature concatenation","volume":"31","author":"Shih","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"107332","DOI":"10.1016\/j.patcog.2020.107332","article-title":"Understanding adversarial attacks on deep learning based medical image analysis systems","volume":"110","author":"Ma","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1109\/TITS.2023.3262347","article-title":"Adversarial attack and defense on deep learning for air transportation communication jamming","volume":"25","author":"Liu","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.future.2021.12.013","article-title":"Ensemble dynamic behavior detection method for adversarial malware","volume":"130","author":"Jing","year":"2022","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"107584","DOI":"10.1016\/j.patcog.2020.107584","article-title":"Universal adversarial perturbations against object detection","volume":"110","author":"Li","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1109\/TAI.2021.3081057","article-title":"Robust vehicle detection in high-resolution aerial images with imbalanced data","volume":"2","author":"Li","year":"2021","journal-title":"IEEE Trans. Artif. Intell."},{"key":"ref_52","first-page":"1","article-title":"Gcevt: Learning global context embedding for vehicle tracking in unmanned aerial vehicle videos","volume":"20","author":"Wu","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4598","DOI":"10.1109\/TMM.2022.3178871","article-title":"AdaZoom: Towards scale-aware large scene object detection","volume":"25","author":"Xu","year":"2022","journal-title":"IEEE Trans. Multimed."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5538","DOI":"10.1109\/JSTARS.2023.3283094","article-title":"Object tracking in UAV videos by multi-feature correlation filters with saliency proposals","volume":"16","author":"Zhang","year":"2023","journal-title":"IEEE J. Select. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_55","first-page":"1","article-title":"OLCN: An optimized low coupling network for small objects detection","volume":"19","author":"Yuan","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2708","DOI":"10.1109\/TIP.2020.3048630","article-title":"Improving single shot object detection with feature scale unmixing","volume":"30","author":"Li","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"4260","DOI":"10.1109\/TIP.2019.2906491","article-title":"Structural similarity-based nonlocal variational models for image restoration","volume":"28","author":"Wang","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3173","DOI":"10.1109\/TCSVT.2023.3234266","article-title":"Only once attack: Fooling the tracker with adversarial template","volume":"33","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"109430","DOI":"10.1016\/j.patcog.2023.109430","article-title":"Quantifying the preferential direction of the model gradient in adversarial training with projected gradient descent","volume":"139","author":"Lanfredi","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"5105","DOI":"10.1109\/JIOT.2023.3301608","article-title":"Improving adversarial robustness with adversarial augmentations","volume":"11","author":"Chen","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"6102","DOI":"10.1109\/TIP.2023.3326398","article-title":"Fast adversarial training with adaptive step size","volume":"32","author":"Huang","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"19153","DOI":"10.1109\/JIOT.2023.3281400","article-title":"Defending Wireless Receivers Against Adversarial Attacks on Modulation Classifiers","volume":"10","author":"Kaddoum","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1109\/TETCI.2022.3193373","article-title":"A new perspective on stabilizing GANs training: Direct adversarial training","volume":"7","author":"Li","year":"2023","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"4417","DOI":"10.1109\/TIP.2022.3184255","article-title":"Boosting fast adversarial training with learnable Adversarial Initialization","volume":"31","author":"Jia","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"3862","DOI":"10.1109\/TIP.2023.3290532","article-title":"Toward intrinsic adversarial robustness through probabilistic training","volume":"32","author":"Dong","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1109\/TASLP.2023.3330613","article-title":"DropAttack: A random dropped weight attack adversarial training for natural language understanding","volume":"32","author":"Ni","year":"2024","journal-title":"IEEE\/ACM Trans. Audio Speech Language Process."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1109\/TNNLS.2022.3183095","article-title":"InfoAT: Improving adversarial training using the information bottleneck principle","volume":"35","author":"Xu","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. 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