Explaining and Harnessing Adversarial Examples.

Articles citing Explaining and Harnessing Adversarial Examples.

Title / Author	Year
Deep learning I Goodfellow, Y Bengio, A Courville MIT press	2016
Towards evaluating the robustness of neural networks N Carlini, D Wagner 2017 ieee symposium on security and privacy (sp), 39-57	2017
Model-agnostic meta-learning for fast adaptation of deep networks C Finn, P Abbeel, S Levine arXiv preprint arXiv:1703.03400	2017
Towards deep learning models resistant to adversarial attacks A Madry, A Makelov, L Schmidt, D Tsipras, A Vladu arXiv preprint arXiv:1706.06083	2017
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images A Nguyen, J Yosinski, J Clune Proceedings of the IEEE conference on computer vision and pattern …	2015
Adversarial examples in the physical world A Kurakin, I Goodfellow, S Bengio arXiv preprint arXiv:1607.02533	2016
Deepfool: a simple and accurate method to fool deep neural networks SM Moosavi-Dezfooli, A Fawzi, P Frossard Proceedings of the IEEE conference on computer vision and pattern …	2016
The limitations of deep learning in adversarial settings N Papernot, P McDaniel, S Jha, M Fredrikson, ZB Celik, A Swami 2016 IEEE European symposium on security and privacy (EuroS&P), 372-387	2016
Distillation as a defense to adversarial perturbations against deep neural networks N Papernot, P McDaniel, X Wu, S Jha, A Swami 2016 IEEE Symposium on Security and Privacy (SP), 582-597	2016
Practical black-box attacks against machine learning N Papernot, P McDaniel, I Goodfellow, S Jha, ZB Celik, A Swami Proceedings of the 2017 ACM on Asia conference on computer and …	2017
Understanding neural networks through deep visualization J Yosinski, J Clune, A Nguyen, T Fuchs, H Lipson arXiv preprint arXiv:1506.06579	2015
Towards principled methods for training generative adversarial networks M Arjovsky, L Bottou arXiv preprint arXiv:1701.04862	2017
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation S Bach, A Binder, G Montavon, F Klauschen, KR Müller, W Samek PloS one 10 (7), e0130140	2015
On large-batch training for deep learning: Generalization gap and sharp minima NS Keskar, D Mudigere, J Nocedal, M Smelyanskiy, PTP Tang arXiv preprint arXiv:1609.04836	2016
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples A Athalye, N Carlini, D Wagner arXiv preprint arXiv:1802.00420	2018
NIPS 2016 tutorial: Generative adversarial networks I Goodfellow arXiv preprint arXiv:1701.00160	2016
Ensemble adversarial training: Attacks and defenses F Tramèr, A Kurakin, N Papernot, I Goodfellow, D Boneh, P McDaniel arXiv preprint arXiv:1705.07204	2017
Deep learning Y Bengio, I Goodfellow, A Courville MIT press	2017
Universal adversarial perturbations SM Moosavi-Dezfooli, A Fawzi, O Fawzi, P Frossard Proceedings of the IEEE conference on computer vision and pattern …	2017
Semi-supervised learning with ladder networks A Rasmus, M Berglund, M Honkala, H Valpola, T Raiko Advances in neural information processing systems, 3546-3554	2015

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