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Recently demonstrated physical-world adversarial attacks have exposed vulnerabilities in perception systems that pose severe risks for safety-critical applications such as autonomous driving.
Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T. G · 1903
Earlier work this paper cites.
On the effectiveness of low frequency perturbations
Sharma, Y., Ding, G. W., and Brubaker, M. A · 1903
Earlier work this paper cites.
Adversarial camera stickers: A physical camera-based attack on deep learning systems
Li, J., Schmidt, F. R., and Kolter, J. Z · 1904
Earlier work this paper cites.
On physical adversarial patches for object detection
Lee, M. and Kolter, J. Z · 1906
Earlier work this paper cites.
Improving robustness without sacrificing accuracy with patch gaussian augmentation
Lopes, R. G., Yin, D., Poole, B., Gilmer, J., and Cubuk, E. D · 1906
Earlier work this paper cites.
Intriguing properties of adversarial training at scale
Xie, C. and Yuille, A · 1906
Earlier work this paper cites.
Defending against physically realizable attacks on image classification
Wu, T., Tong, L., and Vorobeychik, Y · 1909
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2014
Earlier work this paper cites.
Explaining and Harnessing Adversarial Examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
DeepFool: a simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, S.-M., Fawzi, A., and Frossard, P · 2016
Earlier work this paper cites.
A deep learning approach to traffic lights: Detection, tracking, and classification
Behrendt, K. and Novak, L · 2017
Earlier work this paper cites.
Brown, T., Mane, D., Roy, A., Abadi, M., and Gilmer, J · 2017
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
Earlier work this paper cites.
Measuring the tendency of cnns to learn surface statistical regularities
Jo, J. and Bengio, Y · 2017
Earlier work this paper cites.
Adversarial examples in the physical world
Kurakin, A., Goodfellow, I., and Bengio, S · 2017
Cited alongside, same era.
Universal Adversarial Perturbations Against Semantic Image Segmentation
Metzen, J. H., Kumar, M. C., Brox, T., and Fischer, V · 2017
Cited alongside, same era.
Universal adversarial perturbations
Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., and Frossard, P · 2017
Cited alongside, same era.
Fast feature fool: A data independent approach to universal adversarial perturbations
Mopuri, K. R., Garg, U., and Babu, R. V · 2017
Cited alongside, same era.
Synthesizing robust adversarial examples
Athalye, A., Engstrom, L., Ilyas, A., and Kwok, K · 2018
Cited alongside, same era.
Learning universal adversarial perturbations with generative models
Adversarial signboard against object detector
Huang, Y., Kong, A. W. K., and Lam, K.-Y · 2019
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Defending against universal perturbations with shared adversarial training
Mummadi, C. K., Brox, T., and Metzen, J. H · 2019
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Qiao, S., Wang, H., Liu, C., Shen, W., and Yuille, A · 2019
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Attacking optical flow
Ranjan, A., Janai, J., Geiger, A., and Black, M. J · 2019
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Adversarial Patches Exploiting Contextual Reasoning in Object Detection
Saha, A., Subramanya, A., Patil, K., and Pirsiavash, H · 2019
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Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
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Hayes, J. and Danezis, G · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
Cited alongside, same era.
Generalizable data-free objective for crafting universal adversarial perturbations
Mopuri, K. R., Ganeshan, A., and Babu, R. V · 2018
Cited alongside, same era.
On first-order meta-learning algorithms
Nichol, A., Achiam, J., and Schulman, J · 2018
Cited alongside, same era.
Playing the Game of Universal Adversarial Perturbations
Perolat, J., Malinowski, M., Piot, B., and Pietquin, O · 2018
Cited alongside, same era.
Yolov3: An incremental improvement
Redmon, J. and Farhadi, A · 2018
Cited alongside, same era.
Universal adversarial training
Shafahi, A., Najibi, M., Xu, Z., Dickerson, J. P., Davis, L. S., and Goldstein, T · 2018
Cited alongside, same era.
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D · 2019
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Group Normalization
Wu, Y. and He, K · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Yun, S., Han, D., Oh, S. J., Chun, S., Choe, J., and Yoo, Y · 2019
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https://tiny-imagenet.herokuapp.com/
Tiny ImageNet Visual Recognition Challenge · 2020
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APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection
Braunegg, A., Chakraborty, A., Krumdick, M., Lape, N., Leary, S., Manville, K., Merkhofer, E., Strickhart, L., and Walmer, M · 2020
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Certified defenses for adversarial patches
Chiang, P., Ni, R., Abdelkader, A., Zhu, C., Studor, C., and Goldstein, T · 2020
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Sparse-rs: a versatile framework for query-efficient sparse black-box adversarial attacks
Croce, F., Andriushchenko, M., Singh, N. D., Flammarion, N., and Hein, M · 2020
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Improved Adversarial Training via Learned Optimizer
Xiong, Y. and Hsieh, C.-J · 2020
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Efficient adversarial training with transferable adversarial examples
Zheng, H., Zhang, Z., Gu, J., Lee, H., and Prakash, A · 2020
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