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Adversarial examples are malicious inputs crafted to induce misclassification.
Evasion attacks against machine learning at test time
Biggio, B., Corona, I., Maiorca, D., Nelson, B., Šrndić, N., Laskov, P., Giacinto, G., and Roli, F · 2013
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 · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
Earlier work this paper cites.
Conditional generative adversarial nets
Mirza, M. and Osindero, S · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I., Shlens, J., and Szegedy, C · 2015
Earlier work this paper cites.
Adversarial manipulation of deep representations
Sabour, S., Cao, Y., Faghri, F., and Fleet, D. J · 2016
Earlier work this paper cites.
Is attacking machine learning easier than defending it?
Goodfellow, I. and Papernot, N · 2017
Earlier work this paper cites.
The robust manifold defense: Adversarial training using generative models
Ilyas, A., Jalal, A., Asteri, E., Daskalakis, C., and Dimakis, A. G · 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.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
Earlier work this paper cites.
Thwarting adversarial examples: An ℓ 0 \ell_{0} -robust sparse fourier transform
Bafna, M., Murtagh, J., and Vyas, N · 2018
Cited alongside, same era.
Procedural noise adversarial examples for black-box attacks on deep convolutional networks
Co, K. T., Muñoz-González, L., de Maupeou, S., and Lupu, E. C · 2018
Cited alongside, same era.
Motivating the rules of the game for adversarial example research
Gilmer, J., Adams, R. P., Goodfellow, I., Andersen, D., and Dahl, G. E · 2018
Cited alongside, same era.
Certified defenses against adversarial examples
Raghunathan, A., Steinhardt, J., and Liang, P · 2018
Cited alongside, same era.
Defense-gan: Protecting classifiers against adversarial attacks using generative models
Samangouei, P., Kabkab, M., and Chellappa, R · 2018
Learning the difference that makes a difference with counterfactually-augmented data
Kaushik, D., Hovy, E., and Lipton, Z. C · 2019
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Discretization based solutions for secure machine learning against adversarial attacks
Panda, P., Chakraborty, I., and Roy, K · 2019
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Towards the first adversarially robust neural network model on MNIST
Schott, L., Rauber, J., Bethge, M., and Brendel, W · 2019
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Adversarial training and robustness for multiple perturbations
Tramèr, F. and Boneh, D · 2019
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Adversarial: Perceptual ad blocking meets adversarial machine learning
Tramèr, F., Dupré, P., Rusak, G., Pellegrino, G., and Boneh, D · 2019
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Robustness may be at odds with accuracy
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Cited alongside, same era.
On the suitability of lp-norms for creating and preventing adversarial examples
Sharif, M., Bauer, L., and Reiter, M. K · 2018
Cited alongside, same era.
Provable defenses against adversarial examples via the convex outer adversarial polytope
Wong, E. and Kolter, Z · 2018
Cited alongside, same era.
Adversarial examples are not bugs, they are features
Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., and Madry, A · 2019
Cited alongside, same era.
Excessive invariance causes adversarial vulnerability
Jacobsen, J.-H., Behrmann, J., Zemel, R., and Bethge, M · 2019
Cited alongside, same era.
Testing robustness against unforeseen adversaries
Kang, D., Sun, Y., Hendrycks, D., Brown, T., and Steinhardt, J · 2019
Cited alongside, same era.
Adversarial robustness as a prior for learned representations, 2019a
Engstrom, L., Ilyas, A., Santurkar, S., Tsipras, D., Tran, B., and Madry, A
Cited in the paper.
Exploring the landscape of spatial robustness
Engstrom, L., Tran, B., Tsipras, D., Schmidt, L., and Madry, A
Cited in the paper.
Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., and Madry, A · 2019
Later among the works it cites.
A fourier perspective on model robustness in computer vision
Yin, D., Lopes, R. G., Shlens, J., Cubuk, E. D., and Gilmer, J · 2019
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Towards stable and efficient training of verifiably robust neural networks
Zhang, H., Chen, H., Xiao, C., Li, B., Boning, D., and Hsieh, C.-J · 2019
Later among the works it cites.
Towards deep learning models resistant to large perturbations
Shaeiri, A., Nobahari, R., and Rohban, M. H · 2020
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