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The existence of real-world adversarial examples (commonly in the form of patches) poses a serious threat for the use of deep learning models in safety-critical computer vision tasks such as visual perception in autonomous driving.
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2020
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S. Rao, D. Stutz, and B. Schiele, Adversarial Training Against Location-Optimized Adversarial Patches , 01 2020, pp. 429–448
2020
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2021
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2021
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J. H. Metzen, N. Finnie, and R. Hutmacher, “Meta adversarial training against universal patches,” in ICML 2021 Workshop on Adversarial Machine Learning , 2021
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F. Nesti, G. Rossolini, S. Nair, A. Biondi, and G. Buttazzo, “Evaluating the robustness of semantic segmentation for autonomous driving against real-world adversarial patch attacks,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , January 2022, pp. 2280–2289
2022
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2076
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