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We propose a method to learn deep ReLU-based classifiers that are provably robust against norm-bounded adversarial perturbations on the training data.
Robust optimization
Ben-Tal, A., El Ghaoui, L., and Nemirovski, A · 2009
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Robustness and regularization of support vector machines
Xu, H., Caramanis, C., and Mannor, S · 2009
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A public domain dataset for human activity recognition using smartphones
Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J. L · 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., Shlens, J., and Szegedy, C · 2015
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2015
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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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Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
Sharif, M., Bhagavatula, S., Bauer, L., and Reiter, M. K · 2016
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Synthesizing robust adversarial examples
Athalye, A. and Sutskever, I · 2017
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Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
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Ground-truth adversarial examples
Carlini, N., Katz, G., Barrett, C., and Dill, D. L · 2017
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Maximum resilience of artificial neural networks
Cheng, C.-H., Nührenberg, G., and Ruess, H · 2017
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Parseval networks: Improving robustness to adversarial examples
Cisse, M., Bojanowski, P., Grave, E., Dauphin, Y., and Usunier, N · 2017
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Formal verification of piece-wise linear feed-forward neural networks
Ehlers, R · 2017
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A rotation and a translation suffice: Fooling cnns with simple transformations
No need to worry about adversarial examples in object detection in autonomous vehicles
Lu, J., Sibai, H., Fabry, E., and Forsyth, D · 2017
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
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On detecting adversarial perturbations
Metzen, J. H., Genewein, T., Fischer, V., and Bischoff, B · 2017
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Practical black-box attacks against deep learning systems using adversarial examples
Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z. B., and Swami, A · 2017
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Lower bounds on the robustness to adversarial perturbations
Peck, J., Roels, J., Goossens, B., and Saeys, Y · 2017
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Engstrom, L., Tsipras, D., Schmidt, L., and Madry, A · 2017
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Formal guarantees on the robustness of a classifier against adversarial manipulation
Hein, M. and Andriushchenko, M · 2017
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Safety verification of deep neural networks
Huang, X., Kwiatkowska, M., Wang, S., and Wu, M · 2017
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Reluplex: An efficient smt solver for verifying deep neural networks
Katz, G., Barrett, C., Dill, D., Julian, K., and Kochenderfer, M · 2017
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An approach to reachability analysis for feed-forward relu neural networks
Lomuscio, A. and Maganti, L · 2017
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Adversarial examples are not easily detected: Bypassing ten detection methods
Carlini, N. and Wagner, D
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Tjeng, V. and Tedrake, R · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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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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Certified defenses against adversarial examples
Raghunathan, A., Steinhardt, J., and Liang, P · 2018
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Certifiable distributional robustness with principled adversarial training
Sinha, A., Namkoong, H., and Duchi, J · 2018
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