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Object detection techniques for Unmanned Aerial Vehicles (UAVs) rely on Deep Neural Networks (DNNs), which are vulnerable to adversarial attacks.
2010
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Hsieh, M.R., Lin, Y.L., Hsu, W.H.: Drone-based object counting by spatially regularized regional proposal network. In: Proceedings of the IEEE international conference on computer vision. pp. 4145–4153 (2017)
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Xie, C., Wang, J., Zhang, Z., Zhou, Y., Xie, L., Yuille, A.: Adversarial examples for semantic segmentation and object detection. In: Proceedings of the IEEE international conference on computer vision. pp. 1369–1378 (2017)
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Chung, S.J., Paranjape, A.A., Dames, P., Shen, S., Kumar, V.: A survey on aerial swarm robotics. IEEE Transactions on Robotics 34
2018
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Du, D., Qi, Y., Yu, H., Yang, Y., Duan, K., Li, G., Zhang, W., Huang, Q., Tian, Q.: The unmanned aerial vehicle benchmark: Object detection and tracking. In: Proceedings of the European conference on computer vision (ECCV). pp. 370–386 (2018)
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Thys, S., Van Ranst, W., Goedemé, T.: Fooling automated surveillance cameras: adversarial patches to attack person detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. pp. 0–0 (2019)
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Bao, J.: Sparse adversarial attack to object detection. arXiv preprint arXiv:2012.13692 (2020)
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Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33
2020
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Huang, L., Gao, C., Zhou, Y., Xie, C., Yuille, A.L., Zou, C., Liu, N.: Universal physical camouflage attacks on object detectors. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 720–729 (2020)
2020
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Kuang, Q., Wu, J., Pan, J., Zhou, B.: Real-time uav path planning for autonomous urban scene reconstruction. In: 2020 IEEE International Conference on Robotics and Automation (ICRA). pp. 1156–1162. IEEE (2020)
2020
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2020
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Du, A., Chen, B., Chin, T.J., Law, Y.W., Sasdelli, M., Rajasegaran, R., Campbell, D.: Physical adversarial attacks on an aerial imagery object detector. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 1796–1806 (2022)
2022
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2022
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2022
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2022
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2020
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Cao, Y., He, Z., Wang, L., Wang, W., Yuan, Y., Zhang, D., Zhang, J., Zhu, P., Van Gool, L., Han, J., et al.: Visdrone-det2021: The vision meets drone object detection challenge results. In: Proceedings of the IEEE/CVF International conference on computer vision. pp. 2847–2854 (2021)
2021
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Hu, Y.C.T., Kung, B.H., Tan, D.S., Chen, J.C., Hua, K.L., Cheng, W.H.: Naturalistic physical adversarial patch for object detectors. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 7848–7857 (2021)
2021
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Lu, M., Li, Q., Chen, L., Li, H.: Scale-adaptive adversarial patch attack for remote sensing image aircraft detection. Remote Sensing 13
2021
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Tan, J., Ji, N., Xie, H., Xiang, X.: Legitimate adversarial patches: Evading human eyes and detection models in the physical world. In: Proceedings of the 29th ACM international conference on multimedia. pp. 5307–5315 (2021)
2021
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2021
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2021
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Zolfi, A., Kravchik, M., Elovici, Y., Shabtai, A.: The translucent patch: A physical and universal attack on object detectors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15232–15241 (2021)
2021
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Liang, S., Zhang, S., Huang, Y., Zheng, X., Cheng, J., Wu, S.: Data-driven fault diagnosis of fw-uavs with consideration of multiple operation conditions. ISA transactions 126
2022
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Liu, S., Chen, Z., Li, W., Zhu, J., Wang, J., Zhang, W., Gan, Z.: Efficient universal shuffle attack for visual object tracking. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 2739–2743. IEEE (2022)
2022
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2022
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Pavlitskaya, S., Hendl, J., Kleim, S., Müller, L.J., Wylczoch, F., Zöllner, J.M.: Suppress with a patch: Revisiting universal adversarial patch attacks against object detection. In: 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). pp. 1–6. IEEE (2022)
2022
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2022
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Sun, Y., Cao, B., Zhu, P., Hu, Q.: Drone-based rgb-infrared cross-modality vehicle detection via uncertainty-aware learning. IEEE Transactions on Circuits and Systems for Video Technology 32
2022
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Li, S., Zhang, S., Chen, G., Wang, D., Feng, P., Wang, J., Liu, A., Yi, X., Liu, X.: Towards benchmarking and assessing visual naturalness of physical world adversarial attacks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12324–12333 (2023)
2023
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Shrestha, S., Pathak, S., Viegas, E.K.: Towards a robust adversarial patch attack against unmanned aerial vehicles object detection. In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 3256–3263. IEEE (2023)
2023
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Xue, H., Araujo, A., Hu, B., Chen, Y.: Diffusion-based adversarial sample generation for improved stealthiness and controllability. Advances in Neural Information Processing Systems 36
2024
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