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advertorch is a toolbox for adversarial robustness research.
Estimating or propagating gradients through stochastic neurons for conditional computation
Bengio, Y., Léonard, N., and Courville, A. (2013) · 2013
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Is data clustering in adversarial settings secure?
Biggio, B., Pillai, I., Rota Bulò, S., Ariu, D., Pelillo, M., and Roli, F. (2013) · 2013
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Semantic versioning 2.0.0
Preston-Werner, T. (2013) · 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. (2013) · 2013
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C. (2014) · 2014
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Adversarial manipulation of deep representations
Sabour, S., Cao, Y., Faghri, F., and Fleet, D. J. (2015) · 2015
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A study of the effect of jpg compression on adversarial images
Dziugaite, G. K., Ghahramani, Z., and Roy, D. M. (2016) · 2016
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Adversarial machine learning at scale
Kurakin, A., Goodfellow, I., and Bengio, S. (2016) · 2016
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Simple black-box adversarial perturbations for deep networks
Narodytska, N. and Kasiviswanathan, S. P. (2016) · 2016
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The limitations of deep learning in adversarial settings
Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z. B., and Swami, A. (2016b) · 2016
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Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Brendel, W., Rauber, J., and Bethge, M. (2017) · 2017
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Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D. (2017) · 2017
Cited alongside, same era.
Parseval networks: Improving robustness to adversarial examples
Cisse, M., Bojanowski, P., Grave, E., Dauphin, Y., and Usunier, N. (2017) · 2017
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Classification regions of deep neural networks
Fawzi, A., Moosavi-Dezfooli, S.-M., Frossard, P., and Soatto, S. (2017) · 2017
Cited alongside, same era.
Detecting adversarial samples from artifacts
Feinman, R., Curtin, R. R., Shintre, S., and Gardner, A. B. (2017) · 2017
Cited alongside, same era.
Countering adversarial images using input transformations
Guo, C., Rana, M., Cisse, M., and van der Maaten, L. (2017) · 2017
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) · 2018
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Ding, G. W., Sharma, Y., Lui, K. Y. C., and Huang, R. (2018) · 2018
Later among the works it cites.
Boosting adversarial attacks with momentum
Dong, Y., Liao, F., Pang, T., Su, H., Zhu, J., Hu, X., and Li, J. (2018) · 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) · 2018
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Characterizing adversarial subspaces using local intrinsic dimensionality
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Reluplex: An efficient smt solver for verifying deep neural networks
Katz, G., Barrett, C., Dill, D. L., Julian, K., and Kochenderfer, M. J. (2017) · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A. (2017) · 2017
Cited alongside, same era.
On detecting adversarial perturbations
Metzen, J. H., Genewein, T., Fischer, V., and Bischoff, B. (2017) · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2017) · 2017
Cited alongside, same era.
Foolbox: A python toolbox to benchmark the robustness of machine learning models
Rauber, J., Brendel, W., and Bethge, M. (2017) · 2017
Cited alongside, same era.
Feature squeezing: Detecting adversarial examples in deep neural networks
Xu, W., Evans, D., and Qi, Y. (2017) · 2017
Cited alongside, same era.
Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
Papernot, N., McDaniel, P., and Goodfellow, I. (2016a)
Cited in the paper.
Ma, X., Li, B., Wang, Y., Erfani, S. M., Wijewickrema, S., Houle, M. E., Schoenebeck, G., Song, D., and Bailey, J. (2018) · 2018
Later among the works it cites.
Differentiable abstract interpretation for provably robust neural networks
Mirman, M., Gehr, T., and Vechev, M. (2018) · 2018
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Evaluating the robustness of neural networks: An extreme value theory approach
Weng, T.-W., Zhang, H., Chen, P.-Y., Yi, J., Su, D., Gao, Y., Hsieh, C.-J., and Daniel, L. (2018) · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Wong, E. and Kolter, Z. (2018) · 2018
Later among the works it cites.
Spatially transformed adversarial examples
Xiao, C., Zhu, J.-Y., Li, B., He, W., Liu, M., and Song, D. (2018) · 2018
Later among the works it cites.
On the sensitivity of adversarial robustness to input data distributions
Ding, G. W., Lui, K. Y.-C., Jin, X., Wang, L., and Huang, R. (2019) · 2019
Closest in time.
Are adversarial examples inevitable?
Shafahi, A., Huang, W. R., Studer, C., Feizi, S., and Goldstein, T. (2019) · 2019
Closest in time.