| 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 | 2094 | 2015 |
| 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 | 1683 | 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 | 1649 | 2017 |
| Ensemble adversarial training: Attacks and defenses F Tramèr, A Kurakin, N Papernot, I Goodfellow, D Boneh, P McDaniel International Conference on Learning Representations, 2018 | 1176 | 2018 |
| 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 | 885 | 2016 |
| 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 | 533* | 2017 |
| Mixmatch: A holistic approach to semi-supervised learning D Berthelot, N Carlini, I Goodfellow, N Papernot, A Oliver, C Raffel arXiv preprint arXiv:1905.02249, 2019 | 466 | 2019 |
| 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 | 458* | 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 | 416 | 2016 |
| 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 | 379* | 2016 |
| On the (statistical) detection of adversarial examples K Grosse, P Manoharan, N Papernot, M Backes, P McDaniel arXiv preprint arXiv:1702.06280, 2017 | 362 | 2017 |
| 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 | 358 | 2016 |
| Adversarial attacks on neural network policies S Huang, N Papernot, I Goodfellow, Y Duan, P Abbeel arXiv preprint arXiv:1702.02284, 2017 | 341 | 2017 |
| The space of transferable adversarial examples F Tramèr, N Papernot, I Goodfellow, D Boneh, P McDaniel arXiv preprint arXiv:1704.03453, 2017 | 296 | 2017 |
| On evaluating adversarial robustness N Carlini, A Athalye, N Papernot, W Brendel, J Rauber, D Tsipras, ... arXiv preprint arXiv:1902.06705, 2019 | 265 | 2019 |
| Crafting Adversarial Input Sequences for Recurrent Neural Networks N Papernot, P McDaniel, A Swami, R Harang Military Communications Conference, MILCOM, 2016 | 224 | 2016 |
| Making machine learning robust against adversarial inputs I Goodfellow, P McDaniel, N Papernot Communications of the ACM 61 (7), 56-66, 2018 | 207* | 2018 |
| Adversarial examples that fool both computer vision and time-limited humans GF Elsayed, S Shankar, B Cheung, N Papernot, A Kurakin, I Goodfellow, ... arXiv preprint arXiv:1802.08195, 2018 | 207 | 2018 |
| Scalable Private Learning with PATE N Papernot, S Song, I Mironov, A Raghunathan, K Talwar, Ú Erlingsson International Conference on Learning Representations, 2018 | 199 | 2018 |
| Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning N Papernot, P McDaniel arXiv preprint arXiv:1803.04765, 2018 | 188 | 2018 |
Patrick McDanielWilliam L. Weiss Professor, Computer Science and Engineering, Penn State UnviersityVerified email at cse.psu.edu
