עקוב אחר
Nicolas Papernot
Nicolas Papernot
University of Toronto and Vector Institute
כתובת אימייל מאומתת בדומיין utoronto.ca - דף הבית
כותרת
צוטט על ידי
צוטט על ידי
שנה
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
50702015
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
4774*2017
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
38932015
Mixmatch: A holistic approach to semi-supervised learning‏
D Berthelot, N Carlini, I Goodfellow, N Papernot, A Oliver, C Raffel‏
33rd Conference on Neural Information Processing Systems, 2019‏
37062019
Ensemble adversarial training: Attacks and defenses‏
F Tramèr, A Kurakin, N Papernot, I Goodfellow, D Boneh, P McDaniel‏
International Conference on Learning Representations, 2018‏
33722018
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
20892016
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
1275*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
12192016
Adversarial examples for malware detection‏
K Grosse, N Papernot, P Manoharan, M Backes, P McDaniel‏
Computer Security–ESORICS 2017: 22nd European Symposium on Research in …, 2017‏
1164*2017
Adversarial attacks on neural network policies‏
S Huang, N Papernot, I Goodfellow, Y Duan, P Abbeel‏
arXiv preprint arXiv:1702.02284, 2017‏
10442017
On evaluating adversarial robustness‏
N Carlini, A Athalye, N Papernot, W Brendel, J Rauber, D Tsipras, ...‏
arXiv preprint arXiv:1902.06705, 2019‏
10322019
On the (statistical) detection of adversarial examples‏
K Grosse, P Manoharan, N Papernot, M Backes, P McDaniel‏
arXiv preprint arXiv:1702.06280, 2017‏
9072017
Machine unlearning‏
L Bourtoule, V Chandrasekaran, C Choquette-Choo, H Jia, A Travers, ...‏
42nd IEEE Symposium on Security and Privacy, 2019‏
8242019
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
758*2016
Scalable Private Learning with PATE‏
N Papernot, S Song, I Mironov, A Raghunathan, K Talwar, Ú Erlingsson‏
International Conference on Learning Representations, 2018‏
7572018
The space of transferable adversarial examples‏
F Tramèr, N Papernot, I Goodfellow, D Boneh, P McDaniel‏
arXiv preprint arXiv:1704.03453, 2017‏
6782017
Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning‏
N Papernot, P McDaniel‏
arXiv preprint arXiv:1803.04765, 2018‏
6112018
Crafting Adversarial Input Sequences for Recurrent Neural Networks‏
N Papernot, P McDaniel, A Swami, R Harang‏
Military Communications Conference, MILCOM, 2016‏
5492016
Label-Only Membership Inference Attacks‏
CA Choquette Choo, F Tramer, N Carlini, N Papernot‏
38th International Conference on Machine Learning, 2020‏
5212020
Making machine learning robust against adversarial inputs‏
I Goodfellow, P McDaniel, N Papernot‏
Communications of the ACM 61 (7), 56-66, 2018‏
519*2018
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מאמרים 1–20