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Recent breakthroughs in defenses against adversarial examples, like adversarial training, make the neural networks robust against various classes of attackers (e.g., first-order gradient-based attacks).
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.
Towards deep neural network architectures robust to adversarial examples
Gu, S. and Rigazio, L · 2014
Earlier work this paper cites.
Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, N., McDaniel, P., Wu, X., Jha, S., and Swami, A · 2015
Earlier work this paper cites.
Deepfool: a simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, S.-M., Fawzi, A., and Frossard, P · 2016
Earlier work this paper cites.
cleverhans v2. 0.0: an adversarial machine learning library
Papernot, N., Carlini, N., Goodfellow, I., Feinman, R., Faghri, F., Matyasko, A., Hambardzumyan, K., Juang, Y.-L., Kurakin, A., Sheatsley, R., et al · 2016
Earlier work this paper cites.
Mitigating evasion attacks to deep neural networks via region-based classification
Cao, X. and Gong, N. Z · 2017
Earlier work this paper cites.
Parseval networks: Improving robustness to adversarial examples
Cisse, M., Bojanowski, P., Grave, E., Dauphin, Y., and Usunier, N · 2017
Earlier work this paper cites.
Countering adversarial images using input transformations
Guo, C., Rana, M., Cisse, M., and van der Maaten, L · 2017
Earlier work this paper cites.
Adversarial example defenses: Ensembles of weak defenses are not strong
He, W., Wei, J., Chen, X., Carlini, N., and Song, D · 2017
Earlier work this paper cites.
Extending defensive distillation
Papernot, N. and McDaniel, P · 2017
Earlier work this paper cites.
Practical black-box attacks against machine learning
Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z. B., and Swami, A · 2017
Cited alongside, same era.
Deepxplore: Automated whitebox testing of deep learning systems
Pei, K., Cao, Y., Yang, J., and Jana, S · 2017
Cited alongside, same era.
Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Song, Y., Kim, T., Nowozin, S., Ermon, S., and Kushman, N · 2017
Cited alongside, same era.
Mitigating adversarial effects through randomization
Xie, C., Wang, J., Zhang, Z., Ren, Z., and Yuille, A · 2017
Cited alongside, same era.
Efficient defenses against adversarial attacks
Zantedeschi, V., Nicolae, M.-I., and Rawat, A · 2017
Cited alongside, same era.
Ai 2: Safety and robustness certification of neural networks with abstract interpretation
Gehr, T., Mirman, M., Drachsler-Cohen, D., Tsankov, P., Chaudhuri, S., and Vechev, M · 2018
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On the effectiveness of interval bound propagation for training verifiably robust models
Gowal, S., Dvijotham, K., Stanforth, R., Bunel, R., Qin, C., Uesato, J., Mann, T., and Kohli, P · 2018
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Decision boundary analysis of adversarial examples
He, W., Li, B., and Song, D · 2018
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Characterizing adversarial subspaces using local intrinsic dimensionality
Ma, X., Li, B., Wang, Y., Erfani, S. M., Wijewickrema, S., Houle, M. E., Schoenebeck, G., Song, D., and Bailey, J · 2018
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Differentiable abstract interpretation for provably robust neural networks
Mirman, M., Gehr, T., and Vechev, M · 2018
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Synthesizing robust adversarial examples
Athalye, A. and Sutskever, I · 2018
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D · 2018
Cited alongside, same era.
Thermometer encoding: One hot way to resist adversarial examples
Buckman, J., Roy, A., Raffel, C., and Goodfellow, I · 2018
Cited alongside, same era.
Magnet and “efficient defenses against adversarial attacks” are not robust to adversarial examples
Carlini, N. and Wagner, D · 2018
Cited alongside, same era.
Adversarial examples that fool both human and computer vision
Elsayed, G. F., Shankar, S., Cheung, B., Papernot, N., Kurakin, A., Goodfellow, I., and Sohl-Dickstein, J · 2018
Cited alongside, same era.
Adversarial examples are not easily detected: Bypassing ten detection methods
Carlini, N. and Wagner, D
Cited in the paper.
Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D
Cited in the paper.
Later among the works it cites.
Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning
Papernot, N. and McDaniel, P · 2018
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Deeptest: Automated testing of deep-neural-network-driven autonomous cars
Tian, Y., Pei, K., Jana, S., and Ray, B · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Wong, E. and Kolter, J. Z · 2018
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Scaling provable adversarial defenses
Wong, E., Schmidt, F., Metzen, J. H., and Kolter, J. Z · 2018
Later among the works it cites.