| Advances and open problems in federated learning P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... Foundations and Trends® in Machine Learning 14 (1), 2019 | 3388 | 2019 |
| Ensemble Adversarial Training: Attacks and Defenses F Tramèr, A Kurakin, N Papernot, I Goodfellow, D Boneh, P McDaniel International Conference on Learning Representations (ICLR), 2018 | 2473 | 2018 |
| Stealing Machine Learning Models via Prediction APIs F Tramèr, F Zhang, A Juels, MK Reiter, T Ristenpart 25th USENIX security symposium (USENIX Security 16), 601-618, 2016 | 1636 | 2016 |
| On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021 | 1079 | 2021 |
| On evaluating adversarial robustness N Carlini, A Athalye, N Papernot, W Brendel, J Rauber, D Tsipras, ... arXiv preprint arXiv:1902.06705, 2019 | 731 | 2019 |
| On adaptive attacks to adversarial example defenses F Tramèr, N Carlini, W Brendel, A Madry Conference on Neural Information Processing Systems (NeurIPS) 33, 2020 | 606 | 2020 |
| Extracting Training Data from Large Language Models N Carlini, F Tramèr, E Wallace, M Jagielski, A Herbert-Voss, K Lee, ... 30th USENIX Security Symposium (USENIX Security 21), 2633--2650, 2021 | 599 | 2021 |
| The space of transferable adversarial examples F Tramèr, N Papernot, I Goodfellow, D Boneh, P McDaniel arXiv preprint arXiv:1704.03453, 2017 | 532 | 2017 |
| Physical adversarial examples for object detectors K Eykholt, I Evtimov, E Fernandes, B Li, A Rahmati, F Tramèr, A Prakash, ... 12th USENIX Workshop on Offensive Technologies (WOOT 18), 2018 | 380 | 2018 |
| Slalom: Fast, verifiable and private execution of neural networks in trusted hardware F Tramèr, D Boneh International Conference on Learning Representations (ICLR), 2019 | 318 | 2019 |
| Adversarial training and robustness for multiple perturbations F Tramèr, D Boneh Conference on Neural Information Processing Systems (NeurIPS) 32, 2019 | 304 | 2019 |
| Label-Only Membership Inference Attacks CAC Choo, F Tramèr, N Carlini, N Papernot International Conference on Machine Learning (ICML), 1964--1974, 2021 | 213* | 2021 |
| Fairtest: Discovering unwarranted associations in data-driven applications F Tramer, V Atlidakis, R Geambasu, D Hsu, JP Hubaux, M Humbert, ... IEEE European Symposium on Security and Privacy (EuroS&P), 401-416, 2017 | 192* | 2017 |
| Sentinet: Detecting localized universal attacks against deep learning systems E Chou, F Tramèr, G Pellegrino IEEE Security and Privacy Workshops (SPW), 48-54, 2020 | 189* | 2020 |
| Advances and open problems in federated learning P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... arXiv preprint arXiv:1912.04977, 0 | 147* | |
| Differentially Private Learning Needs Better Features (or Much More Data) F Tramèr, D Boneh International Conference on Learning Representations (ICLR), 2021 | 135 | 2021 |
| Membership Inference Attacks From First Principles N Carlini, S Chien, M Nasr, S Song, A Terzis, F Tramèr 43rd IEEE Symposium on Security and Privacy (S&P 2022), 2022 | 119 | 2022 |
| Sealed-glass proofs: Using transparent enclaves to prove and sell knowledge F Tramèr, F Zhang, H Lin, JP Hubaux, A Juels, E Shi IEEE European Symposium on Security and Privacy (EuroS&P), 19-34, 2017 | 114 | 2017 |
| Formal abstractions for attested execution secure processors R Pass, E Shi, F Tramèr Annual International Conference on the Theory and Applications of …, 2017 | 108 | 2017 |
| Fundamental tradeoffs between invariance and sensitivity to adversarial perturbations F Tramèr, J Behrmann, N Carlini, N Papernot, JH Jacobsen International Conference on Machine Learning (ICML), 9561-9571, 2020 | 106* | 2020 |
Nicholas CarliniGoogle Brain在 google.com 的电子邮件经过验证
