| 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 | 1980 | 2018 |
| 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 | 1914 | 2019 |
| 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 | 1223 | 2016 |
| On evaluating adversarial robustness N Carlini, A Athalye, N Papernot, W Brendel, J Rauber, D Tsipras, ... arXiv preprint arXiv:1902.06705, 2019 | 545 | 2019 |
| The space of transferable adversarial examples F Tramèr, N Papernot, I Goodfellow, D Boneh, P McDaniel arXiv preprint arXiv:1704.03453, 2017 | 443 | 2017 |
| On adaptive attacks to adversarial example defenses F Tramèr, N Carlini, W Brendel, A Madry Conference on Neural Information Processing Systems (NeurIPS) 33, 2020 | 412 | 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 | 275 | 2021 |
| 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 | 269 | 2018 |
| 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 | 235 | 2021 |
| Slalom: Fast, verifiable and private execution of neural networks in trusted hardware F Tramèr, D Boneh International Conference on Learning Representations (ICLR), 2019 | 230 | 2019 |
| Adversarial training and robustness for multiple perturbations F Tramèr, D Boneh Conference on Neural Information Processing Systems (NeurIPS) 32, 2019 | 218 | 2019 |
| Sentinet: Detecting localized universal attacks against deep learning systems E Chou, F Tramèr, G Pellegrino IEEE Security and Privacy Workshops (SPW), 48-54, 2020 | 120* | 2020 |
| 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 | 120 | 2017 |
| Label-Only Membership Inference Attacks CAC Choo, F Tramèr, N Carlini, N Papernot International Conference on Machine Learning (ICML), 1964--1974, 2021 | 103* | 2021 |
| 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 | 97 | 2017 |
| Differential privacy with bounded priors: reconciling utility and privacy in genome-wide association studies F Tramèr, Z Huang, JP Hubaux, E Ayday 22nd ACM SIGSAC Conference on Computer and Communications Security (CCS …, 2015 | 96 | 2015 |
| Formal abstractions for attested execution secure processors R Pass, E Shi, F Tramèr Annual International Conference on the Theory and Applications of …, 2017 | 87 | 2017 |
| Privateride: A privacy-enhanced ride-hailing service A Pham, I Dacosta, B Jacot-Guillarmod, K Huguenin, T Hajar, F Tramèr, ... Proceedings on Privacy Enhancing Technologies 2017 (2), 38–56, 2017 | 81 | 2017 |
| Enter the Hydra: Towards Principled Bug Bounties and Exploit-Resistant Smart Contracts L Breidenbach, P Daian, F Tramèr, A Juels 27th USENIX Security Symposium (USENIX Security 18), 1335-1352, 2018 | 77* | 2018 |
| 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 | 76* | 2020 |
Dan BonehProfessor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
