| 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 | 3755 | 2015 |
| 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 | 3550* | 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 | 2929 | 2015 |
| Ensemble adversarial training: Attacks and defenses F Tramèr, A Kurakin, N Papernot, I Goodfellow, D Boneh, P McDaniel International Conference on Learning Representations, 2018 | 2364 | 2018 |
| 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 | 2105 | 2019 |
| 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 | 1574 | 2016 |
| 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 | 923* | 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 | 913* | 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 | 823 | 2016 |
| Adversarial attacks on neural network policies S Huang, N Papernot, I Goodfellow, Y Duan, P Abbeel arXiv preprint arXiv:1702.02284, 2017 | 723 | 2017 |
| On evaluating adversarial robustness N Carlini, A Athalye, N Papernot, W Brendel, J Rauber, D Tsipras, ... arXiv preprint arXiv:1902.06705, 2019 | 694 | 2019 |
| On the (statistical) detection of adversarial examples K Grosse, P Manoharan, N Papernot, M Backes, P McDaniel arXiv preprint arXiv:1702.06280, 2017 | 677 | 2017 |
| 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 | 614* | 2016 |
| The space of transferable adversarial examples F Tramèr, N Papernot, I Goodfellow, D Boneh, P McDaniel arXiv preprint arXiv:1704.03453, 2017 | 501 | 2017 |
| Scalable Private Learning with PATE N Papernot, S Song, I Mironov, A Raghunathan, K Talwar, Ú Erlingsson International Conference on Learning Representations, 2018 | 476 | 2018 |
| Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning N Papernot, P McDaniel arXiv preprint arXiv:1803.04765, 2018 | 437 | 2018 |
| Crafting Adversarial Input Sequences for Recurrent Neural Networks N Papernot, P McDaniel, A Swami, R Harang Military Communications Conference, MILCOM, 2016 | 421 | 2016 |
| Making machine learning robust against adversarial inputs I Goodfellow, P McDaniel, N Papernot Communications of the ACM 61 (7), 56-66, 2018 | 392* | 2018 |
| 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 31, 2018 | 298 | 2018 |
| High accuracy and high fidelity extraction of neural networks M Jagielski, N Carlini, D Berthelot, A Kurakin, N Papernot Proceedings of the 29th USENIX Conference on Security Symposium, 1345-1362, 2020 | 241* | 2020 |
Patrick McDanielWilliam L. Weiss Professor, Computer Science and Engineering, Penn State UnviersityZweryfikowany adres z cse.psu.edu
