Nicolas Papernot
Nicolas Papernot
University of Toronto and Vector Institute
Verified email at utoronto.ca - Homepage
Title
Cited by
Cited by
Year
The Limitations of Deep Learning in Adversarial Settings
N Papernot, P McDaniel, S Jha, M Fredrikson, ZB Celik, A Swami
Proceedings of the 1st IEEE European Symposium on Security and Privacy, 2015
18022015
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
N Papernot, P McDaniel, X Wu, S Jha, A Swami
Proceedings of the 37th IEEE Symposium on Security and Privacy, 2015
14522015
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
14262017
Ensemble adversarial training: Attacks and defenses
F Tramèr, A Kurakin, N Papernot, I Goodfellow, D Boneh, P McDaniel
International Conference on Learning Representations, 2018
9942018
Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
N Papernot, P McDaniel, I Goodfellow
arXiv preprint arXiv:1605.07277, 2016
7452016
Adversarial examples for malware detection
K Grosse, N Papernot, P Manoharan, M Backes, P McDaniel
European Symposium on Research in Computer Security, 62-79, 2017
464*2017
SoK: Towards the Science of Security and Privacy in Machine Learning
N Papernot, P McDaniel, A Sinha, MP Wellman
2018 IEEE European Symposium on Security and Privacy (EuroS&P), 2018
378*2018
Semi-supervised knowledge transfer for deep learning from private training data
N Papernot, M Abadi, Ú Erlingsson, I Goodfellow, K Talwar
Proceedings of the 5th International Conference on Learning Representations …, 2016
3492016
Technical report on the cleverhans v2. 1.0 adversarial examples library
N Papernot, F Faghri, N Carlini, I Goodfellow, R Feinman, A Kurakin, ...
arXiv preprint arXiv:1610.00768, 2016
346*2016
Practical black-box attacks against deep learning systems using adversarial examples
N Papernot, P McDaniel, I Goodfellow, S Jha, ZB Celik, A Swami
arXiv preprint arXiv:1602.02697 1 (2), 3, 2016
3352016
Mixmatch: A holistic approach to semi-supervised learning
D Berthelot, N Carlini, I Goodfellow, N Papernot, A Oliver, CA Raffel
Advances in Neural Information Processing Systems, 5049-5059, 2019
3252019
On the (statistical) detection of adversarial examples
K Grosse, P Manoharan, N Papernot, M Backes, P McDaniel
arXiv preprint arXiv:1702.06280, 2017
3182017
Adversarial attacks on neural network policies
S Huang, N Papernot, I Goodfellow, Y Duan, P Abbeel
arXiv preprint arXiv:1702.02284, 2017
2822017
The space of transferable adversarial examples
F Tramèr, N Papernot, I Goodfellow, D Boneh, P McDaniel
arXiv preprint arXiv:1704.03453, 2017
2512017
Crafting Adversarial Input Sequences for Recurrent Neural Networks
N Papernot, P McDaniel, A Swami, R Harang
Military Communications Conference, MILCOM, 2016
1882016
On evaluating adversarial robustness
N Carlini, A Athalye, N Papernot, W Brendel, J Rauber, D Tsipras, ...
arXiv preprint arXiv:1902.06705, 2019
1872019
Making machine learning robust against adversarial inputs
I Goodfellow, P McDaniel, N Papernot
Communications of the ACM 61 (7), 56-66, 2018
167*2018
Scalable Private Learning with PATE
N Papernot, S Song, I Mironov, A Raghunathan, K Talwar, Ú Erlingsson
International Conference on Learning Representations, 2018
1652018
Adversarial examples that fool both computer vision and time-limited humans
G Elsayed, S Shankar, B Cheung, N Papernot, A Kurakin, I Goodfellow, ...
Advances in Neural Information Processing Systems, 3910-3920, 2018
1642018
Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning
N Papernot, P McDaniel
arXiv preprint arXiv:1803.04765, 2018
1462018
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