Towards Robust Neural Networks via Random Self-ensemble

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2012

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Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. In: International Conference on Learning Representation (2014)

2014

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Goodfellow, I., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: International Conference on Learning Representations (2015), http://arxiv.org/abs/1412.6572

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2015

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Huang, R., Xu, B., Schuurmans, D., Szepesvári, C.: Learning with a strong adversary. arXiv preprint arXiv:1511.03034 (2015)

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2015

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2015

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Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1–9 (2015)

2015

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Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., Bengio, Y.: Show, attend and tell: Neural image caption generation with visual attention. In: International Conference on Machine Learning. pp. 2048–2057 (2015)

2015

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He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)

2016

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Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z.B., Swami, A.: Practical black-box attacks against deep learning systems using adversarial examples. arXiv preprint arXiv:1602.02697 (2016)

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2016

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Papernot, N., McDaniel, P., Wu, X., Jha, S., Swami, A.: Distillation as a defense to adversarial perturbations against deep neural networks. In: Security and Privacy (SP), 2016 IEEE Symposium on. pp. 582–597. IEEE (2016)

2016

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Carlini, N., Wagner, D.: Adversarial examples are not easily detected: Bypassing ten detection methods. In: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security. pp. 3–14. AISec ’17, ACM, New York, NY, USA (2017). https://doi.org/10.1145/3128572.3140444, http://doi.acm.org/10.1145/3128572.3140444

2017

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Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: Security and Privacy (SP), 2017 IEEE Symposium on. pp. 39–57. IEEE (2017)

2017

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Feinman, R., Curtin, R.R., Shintre, S., Gardner, A.B.: Detecting adversarial samples from artifacts. arXiv preprint arXiv:1703.00410 (2017)

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2017

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Grosse, K., Manoharan, P., Papernot, N., Backes, M., McDaniel, P.: On the (statistical) detection of adversarial examples. arXiv preprint arXiv:1702.06280 (2017)

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2017

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Hein, M., Andriushchenko, M.: Formal guarantees on the robustness of a classifier against adversarial manipulation. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, USA. pp. 2263–2273 (2017)

2017

Cited alongside, same era.

Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial machine learning at scale. In: International Conference on Learning Representations (ICLR) (2017)

2017

Cited alongside, same era.

Meng, D., Chen, H.: Magnet: A two-pronged defense against adversarial examples. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. pp. 135–147. CCS ’17, ACM, New York, NY, USA (2017). https://doi.org/10.1145/3133956.3134057, http://doi.acm.org/10.1145/3133956.3134057

2017

Cited alongside, same era.

Noh, H., You, T., Mun, J., Han, B.: Regularizing deep neural networks by noise: Its interpretation and optimization. In: Advances in Neural Information Processing Systems. pp. 5115–5124 (2017)

Dhillon, G.S., Azizzadenesheli, K., Bernstein, J.D., Kossaifi, J., Khanna, A., Lipton, Z.C., Anandkumar, A.: Stochastic activation pruning for robust adversarial defense. In: International Conference on Learning Representations (2018), https://openreview.net/forum?id=H1uR4GZRZ

2018

Closest in time.

Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., Song, D.: Robust physical-world attacks on deep learning visual classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1625–1634 (2018)

2018

Closest in time.

Guo, C., Rana, M., Cisse, M., van der Maaten, L.: Countering adversarial images using input transformations. In: International Conference on Learning Representations (2018), https://openreview.net/forum?id=SyJ7ClWCb

2018

Closest in time.

Lecuyer, M., Atlidakis, V., Geambasu, R., Hsu, D., Jana, S.: Certified Robustness to Adversarial Examples with Differential Privacy. ArXiv e-prints (Feb 2018)