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Adversarial patches are images designed to fool otherwise well-performing neural network-based computer vision models.
2013
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T. N. Mundhenk, G. Konjevod, W. A. Sakla, and K. Boakye, A large contextual dataset for classification, detection and counting of cars with deep learning
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T. B. Brown, D. Mané, A. Roy, M. Abadi, and J. Gilmer, Adversarial patch
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2018
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2018
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S.-T. Chen, C. Cornelius, J. Martin, and D. H. P. Chau, Shapeshifter: Robust physical adversarial attack on faster r-cnn object detector
2018
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2018
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2018
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S. Thys, W. Van Ranst, and T. Goedemé, Fooling automated surveillance cameras: adversarial patches to attack person detection
2019
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2019
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A. Braunegg, A. Chakraborty, M. Krumdick, N. Lape, S. Leary, K. Manville, E. Merkhofer, L. Strickhart, and M. Walmer, Apricot: A dataset of physical adversarial attacks on object detection
2020
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Z. Wu, S.-N. Lim, L. S. Davis, and T. Goldstein, Making an invisibility cloak: Real world adversarial attacks on object detectors
2020
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K. Xu, G. Zhang, S. Liu, Q. Fan, M. Sun, H. Chen, P.-Y. Chen, Y. Wang, and X. Lin, Adversarial t-shirt! evading person detectors in a physical world
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2018
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X. Yuan, P. He, Q. Zhu, and X. Li, Adversarial examples: Attacks and defenses for deep learning
2019
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M. Lee and Z. Kolter, On physical adversarial patches for object detection
2019
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2020
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G. S. Hartnett, L. Menthe, J. Léveillé, D. Baveye, Z. L. Ang, D. Gold, J. Hagen, and J. Xu, Operationally relevant artificial training for machine learning
2020
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Y. Wang, H. Lv, X. Kuang, G. Zhao, Y.-a. Tan, Q. Zhang, and J. Hu, Towards a physical-world adversarial patch for blinding object detection models
2021
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