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Adversarial training (AT) is a widely recognized defense mechanism to gain the robustness of deep neural networks against adversarial attacks.
You only propagate once: Accelerating adversarial training via maximal principle
Zhang, D., Zhang, T., Lu, Y., Zhu, Z., and Dong, B · 1905
Earlier work this paper cites.
A penalty function approach for solving bi-level linear programs
White, D. J. and Anandalingam, G · 1993
Earlier work this paper cites.
Descent approaches for quadratic bilevel programming
Vicente, L., Savard, G., and Júdice, J · 1994
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Foundations of bilevel programming
Dempe, S · 2002
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Efficient robust training via backward smoothing
Chen, J., Cheng, Y., Gan, Z., Gu, Q., and Liu, J · 2010
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I., Shlens, J., and Szegedy, C · 2015
Earlier work this paper cites.
Gould, S., Fernando, B., Cherian, A., Anderson, P., Cruz, R. S., and Guo, E · 2016
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The limitations of deep learning in adversarial settings
Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z. B., and Swami, A · 2016
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Zagoruyko, S. and Komodakis, N · 2016
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Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
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Searching for activation functions
Ramachandran, P., Zoph, B., and Le, Q. V · 2017
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Evaluating and understanding the robustness of adversarial logit pairing
Engstrom, L., Ilyas, A., and Athalye, A · 2018
Cited alongside, same era.
Approximation methods for bilevel programming
Ghadimi, S. and Wang, M · 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.
Unlabeled data improves adversarial robustness
Carmon, Y., Raghunathan, A., Schmidt, L., Liang, P., and Duchi, J. C · 2019
Cited alongside, same era.
Research on distributed renewable energy transaction decision-making based on multi-agent bilevel cooperative reinforcement learning
Chen, Z., Liu, D., Wu, X., and Xu, X · 2019
Cited alongside, same era.
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Croce, F. and Hein, M · 2020
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An analysis of adversarial attacks and defenses on autonomous driving models
Deng, Y., Zheng, X., Zhang, T., Chen, C., Lou, G., and Kim, M · 2020
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Hong, M., Wai, H.-T., Wang, Z., and Yang, Z · 2020
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Metapoison: Practical general-purpose clean-label data poisoning
Huang, W. R., Geiping, J., Fowl, L., Taylor, G., and Goldstein, T · 2020
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Bilevel optimization: Nonasymptotic analysis and faster algorithms
Ji, K., Yang, J., and Liang, Y · 2020
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Robustness via curvature regularization, and vice versa
Moosavi-Dezfooli, S.-M., Fawzi, A., Uesato, J., and Frossard, P · 2019
Cited alongside, same era.
Robust optimization over multiple domains
Qian, Q., Zhu, S., Tang, J., Jin, R., Sun, B., and Li, H · 2019
Cited alongside, same era.
Meta-learning with implicit gradients
Rajeswaran, A., Finn, C., Kakade, S., and Levine, S · 2019
Cited alongside, same era.
Provably robust deep learning via adversarially trained smoothed classifiers
Salman, H., Yang, G., Li, J., Zhang, P., Zhang, H., Razenshteyn, I., and Bubeck, S · 2019
Cited alongside, same era.
Adversarial training for free!
Shafahi, A., Najibi, M., Ghiasi, M. A., Xu, Z., Dickerson, J., Studer, C., Davis, L. S., Taylor, G., and Goldstein, T · 2019
Cited alongside, same era.
Fooling automated surveillance cameras: adversarial patches to attack person detection
Thys, S., Van Ranst, W., and Goedemé, T · 2019
Cited alongside, same era.
Feature denoising for improving adversarial robustness
Xie, C., Wu, Y., Maaten, L. v. d., Yuille, A. L., and He, K · 2019
Cited alongside, same era.
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Black-box adversarial attacks in autonomous vehicle technology
Kumar, K. N., Vishnu, C., Mitra, R., and Mohan, C. K · 2020
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Towards understanding fast adversarial training
Li, B., Wang, S., Jana, S., and Carin, L · 2020
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Nonconvex min-max optimization: Applications, challenges, and recent theoretical advances
Razaviyayn, M., Huang, T., Lu, S., Nouiehed, M., Sanjabi, M., and Hong, M · 2020
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Overfitting in adversarially robust deep learning
Rice, L., Wong, E., and Kolter, Z · 2020
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Fast is better than free: Revisiting adversarial training
Wong, E., Rice, L., and Kolter, J. Z · 2020
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Adversarial T-Shirt! evading person detectors in a physical world
Xu, K., Zhang, G., Liu, S., Fan, Q., Sun, M., Chen, H., Chen, P.-Y., Wang, Y., and Lin, X · 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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A momentum-assisted single-timescale stochastic approximation algorithm for bilevel optimization
Khanduri, P., Zeng, S., Hong, M., Wai, H.-T., Wang, Z., and Yang, Z · 2021
Closest in time.
Adversarial regularization as stackelberg game: An unrolled optimization approach
Zuo, S., Liang, C., Jiang, H., Liu, X., He, P., Gao, J., Chen, W., and Zhao, T · 2021
Closest in time.