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Adversarial robustness continues to be a major challenge for deep learning.
Advertorch v0. 1: An adversarial robustness toolbox based on pytorch
Ding, G. W., Wang, L., and Jin, X · 1902
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 1903
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Adversarial training and robustness for multiple perturbations
Tramèr, F. and Boneh, D · 1904
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Functional adversarial attacks
Laidlaw, C. and Feizi, S · 1906
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Accurate, reliable and fast robustness evaluation
Brendel, W., Rauber, J., Kümmerer, M., Ustyuzhaninov, I., and Bethge, M · 1907
Earlier work this paper cites.
Adversarial robustness against the union of multiple perturbation models
Maini, P., Wong, E., and Kolter, Z · 1909
Earlier work this paper cites.
Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 1911
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Square attack: a query-efficient black-box adversarial attack via random search
Andriushchenko, M., Croce, F., Flammarion, N., and Hein, M · 1912
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Secure and robust machine learning for healthcare: A survey
Qayyum, A., Qadir, J., Bilal, M., and Al-Fuqaha, A · 2001
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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 · 2002
Earlier work this paper cites.
Overfitting in adversarially robust deep learning
Rice, L., Wong, E., and Kolter, Z · 2002
Earlier work this paper cites.
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Croce, F. and Hein, M · 2003
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Out-of-distribution generalization via risk extrapolation (rex)
Krueger, D., Caballero, E., Jacobsen, J.-H., Zhang, A., Binas, J., Zhang, D., Le Priol, R., and Courville, A · 2003
Earlier work this paper cites.
Detecting change in data streams
Kifer, D., Ben-David, S., and Gehrke, J · 2004
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Adversarial feature desensitization
Bashivan, P., Bayat, R., Ibrahim, A., Ahuja, K., Faramarzi, M., Laleh, T., Richards, B., and Rish, I · 2006
Cited alongside, same era.
The impact of non-stationarity on generalisation in deep reinforcement learning
Igl, M., Farquhar, G., Luketina, J., Boehmer, W., and Whiteson, S · 2006
Cited alongside, same era.
Perceptual adversarial robustness: Defense against unseen threat models
Laidlaw, C., Singla, S., and Feizi, S · 2006
Cited alongside, same era.
In search of lost domain generalization
Gulrajani, I. and Lopez-Paz, D · 2007
Cited alongside, same era.
Deepfool: a simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, S.-M., Fawzi, A., and Frossard, P · 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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Adversarial examples in the physical world
Kurakin, A., Goodfellow, I., and Bengio, S · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
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Salman, H., Ilyas, A., Engstrom, L., Kapoor, A., and Madry, A · 2007
Cited alongside, same era.
Adversarially-trained deep nets transfer better: Illustration on image classification
Utrera, F., Kravitz, E., Erichson, N. B., Khanna, R., and Mahoney, M. W · 2007
Cited alongside, same era.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2010
Cited alongside, same era.
Bag of tricks for adversarial training
Pang, T., Yang, X., Dong, Y., Su, H., and Zhu, J · 2010
Cited alongside, same era.
Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
Cited alongside, same era.
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
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Cited alongside, same era.
Athalye, A., Carlini, N., and Wagner, D · 2018
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Robust physical-world attacks on deep learning visual classification
Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., and Song, D · 2018
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On the geometry of adversarial examples
Khoury, M. and Hadfield-Menell, D · 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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Improving the generalization of adversarial training with domain adaptation
Song, C., He, K., Wang, L., and Hopcroft, J. E · 2018
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Spatially transformed adversarial examples
Xiao, C., Zhu, J.-Y., Li, B., He, W., Liu, M., and Song, D · 2018
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Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
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Practical adversarial attacks against speaker recognition systems
Li, Z., Shi, C., Xie, Y., Liu, J., Yuan, B., and Chen, Y · 2020
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Generalizing to unseen domains: A survey on domain generalization
Wang, J., Lan, C., Liu, C., Ouyang, Y., Zeng, W., and Qin, T · 2021
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Revisiting weakly supervised pre-training of visual perception models
Singh, M., Gustafson, L., Adcock, A., de Freitas Reis, V., Gedik, B., Kosaraju, R. P., Mahajan, D., Girshick, R., Dollár, P., and Van Der Maaten, L · 2022
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