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Adaptive defenses, which optimize at test time, promise to improve adversarial robustness.
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
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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. J., Shlens, J., and Szegedy, C · 2015
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Deep Learning
Goodfellow, I., Bengio, Y., and Courville, A · 2016
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Adversarial examples in the physical world
Kurakin, A., Goodfellow, I., and Bengio, S · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, N., McDaniel, P., Wu, X., Jha, S., and Swami, A · 2016
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Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
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Synthesizing robust adversarial examples
Athalye, A. and Sutskever, I · 2018
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D · 2018
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JAX: composable transformations of Python+NumPy programs, 2018
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., and Wanderman-Milne, S · 2018
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Thermometer Encoding: One Hot Way To Resist Adversarial Examples
Buckman, J., Roy, A., Raffel, C., and Goodfellow, I · 2018
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Countering Adversarial Images using Input Transformations
Guo, C., Rana, M., Cisse, M., and van der Maaten, L · 2018
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Kannan, H., Kurakin, A., and Goodfellow, I · 2018
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
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Defense-gan: Protecting classifiers against adversarial attacks using generative models
Samangouei, P., Kabkab, M., and Chellappa, R · 2018
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Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Song, Y., Kim, T., Nowozin, S., Ermon, S., and Kushman, N · 2018
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Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
Uesato, J., O’Donoghue, B., Oord, A. v. d., and Kohli, P · 2018
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On Evaluating Adversarial Robustness
Carlini, N., Athalye, A., Papernot, N., Brendel, W., Rauber, J., Tsipras, D., Goodfellow, I., and Madry, A · 2019
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Unlabeled data improves adversarial robustness
Carmon, Y., Raghunathan, A., Schmidt, L., Duchi, J. C., and Liang, P. S · 2019
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Are generative classifiers more robust to adversarial attacks?
Li, Y., Bradshaw, J., and Sharma, Y · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
Cited alongside, same era.
Towards the first adversarially robust neural network model on mnist
Schott, L., Rauber, J., Bethge, M., and Brendel, W · 2019
Cited alongside, same era.
Me-net: Towards effective adversarial robustness with matrix estimation
Yang, Y., Zhang, G., Katabi, D., and Xu, Z · 2019
Cited alongside, same era.
Understanding and Improving Fast Adversarial Training
Andriushchenko, M. and Flammarion, N · 2020
Cited alongside, same era.
Square Attack: a query-efficient black-box adversarial attack via random search
Joint inference and input optimization in equilibrium networks
Gurumurthy, S., Bai, S., Manchester, Z., and Kolter, J. Z · 2021
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Stochastic security: Adversarial defense using long-run dynamics of energy-based models
Hill, M., Mitchell, J., and Zhu, S.-C · 2021
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Aid-purifier: A light auxiliary network for boosting adversarial defense
Hwang, D., Lee, E., and Rhee, W · 2021
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Stable neural ode with lyapunov-stable equilibrium points for defending against adversarial attacks
Kang, Q., Song, Y., Ding, Q., and Tay, W. P · 2021
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Adversarial attacks are reversible with natural supervision
Mao, C., Chiquier, M., Wang, H., Yang, J., and Vondrick, C · 2021
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Adversarially trained models with test-time covariate shift adaptation
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Andriushchenko, M., Croce, F., Flammarion, N., and Hein, M · 2020
Cited alongside, same era.
RayS: A ray searching method for hard-label adversarial attack
Chen, J. and Gu, Q · 2020
Cited alongside, same era.
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Croce, F. and Hein, M · 2020
Cited alongside, same era.
Uncovering the limits of adversarial training against norm-bounded adversarial examples
Gowal, S., Qin, C., Uesato, J., Mann, T., and Kohli, P · 2020
Cited alongside, same era.
Mixup inference: Better exploiting mixup to defend adversarial attacks
Pang, T., Xu, K., and Zhu, J · 2020
Cited alongside, same era.
Overfitting in adversarially robust deep learning
Rice, L., Wong, E., and Kolter, J. Z · 2020
Cited alongside, same era.
Online adversarial purification based on self-supervised learning
Shi, C., Holtz, C., and Mishne, G · 2020
Cited alongside, same era.
Nandy, J., Saha, S., Hsu, W., Lee, M. L., and Zhu, X. X · 2021
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Indicators of attack failure: Debugging and improving optimization of adversarial examples
Pintor, M., Demetrio, L., Sotgiu, A., Manca, G., Demontis, A., Carlini, N., Biggio, B., and Roli, F · 2021
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Improving model robustness with latent distribution locally and globally
Qian, Z., Zhang, S., Huang, K., Wang, Q., Zhang, R., and Yi, X · 2021
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Helper-based adversarial training: Reducing excessive margin to achieve a better accuracy vs. robustness trade-off
Rade, R. and Moosavi-Dezfooli, S.-M · 2021
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Fixing data augmentation to improve adversarial robustness
Rebuffi, S.-A., Gowal, S., Calian, D. A., Stimberg, F., Wiles, O., and Mann, T · 2021
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Attacking adversarial attacks as a defense
Wu, B., Pan, H., Shen, L., Gu, J., Zhao, S., Li, Z., Cai, D., He, X., and Liu, W · 2021
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Adversarial purification with score-based generative models
Yoon, J., Hwang, S. J., and Lee, J · 2021
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Theoretically Principled Trade-off between Robustness and Accuracy
Zhang, H., Yu, Y., Jiao, J., Xing, E. P., Ghaoui, L. E., and Jordan, M. I · 2021
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Combating adversaries with anti-adversaries
Alfarra, M., Perez, J. C., Thabet, A., Bibi, A., Torr, P., and Ghanem, B · 2022
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Towards evaluating the robustness of neural networks learned by transduction
Chen, J., Wu, X., Guo, Y., Liang, Y., and Jha, S · 2022
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Diffusion models for adversarial purification
Nie, W., Guo, B., Huang, Y., Xiao, C., Vahdat, A., and Anandkumar, A · 2022
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AGS: Attribution guided sharpening as a defense against adversarial attacks
Perez Tobia, J., Braun, P., and Narayan, A · 2022
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Hindering adversarial attacks with implicit neural representations
Rusu, A. A., Calian, D. A., Gowal, S., and Hadsell, R · 2022
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