| 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 | 5245 | 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 | 3090 | 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 | 2917 | 2021 |
| 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 | 2042 | 2016 |
| 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 | 1252 | 2021 |
| On evaluating adversarial robustness N Carlini, A Athalye, N Papernot, W Brendel, J Rauber, D Tsipras, ... arXiv preprint arXiv:1902.06705, 2019 | 919 | 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 | 815 | 2020 |
| The space of transferable adversarial examples F Tramèr, N Papernot, I Goodfellow, D Boneh, P McDaniel arXiv preprint arXiv:1704.03453, 2017 | 633 | 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 | 483 | 2018 |
| Slalom: Fast, verifiable and private execution of neural networks in trusted hardware F Tramèr, D Boneh International Conference on Learning Representations (ICLR), 2019 | 405 | 2019 |
| Label-Only Membership Inference Attacks CAC Choo, F Tramèr, N Carlini, N Papernot International Conference on Machine Learning (ICML), 1964--1974, 2021 | 399* | 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 | 388 | 2022 |
| Adversarial training and robustness for multiple perturbations F Tramèr, D Boneh Conference on Neural Information Processing Systems (NeurIPS) 32, 2019 | 387 | 2019 |
| Quantifying memorization across neural language models N Carlini, D Ippolito, M Jagielski, K Lee, F Tramèr, C Zhang International Conference on Learning Representations (ICLR), 2023 | 382 | 2023 |
| Sentinet: Detecting localized universal attacks against deep learning systems E Chou, F Tramer, G Pellegrino 2020 IEEE Security and Privacy Workshops (SPW), 48-54, 2020 | 323 | 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 | 306* | |
| Extracting training data from diffusion models N Carlini, J Hayes, M Nasr, M Jagielski, V Sehwag, F Tramer, B Balle, ... 32nd USENIX Security Symposium (USENIX Security 23), 5253-5270, 2023 | 298 | 2023 |
| Large language models can be strong differentially private learners X Li, F Tramèr, P Liang, T Hashimoto International Conference on Learning Representations (ICLR), 2022 | 234 | 2022 |
| On the opportunities and risks of foundation models. CoRR abs/2108.07258 (2021) R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... Preprint at https://arxiv. org/abs/2108.07258 2108, 2021 | 223* | 2021 |
| Differentially Private Learning Needs Better Features (or Much More Data) F Tramèr, D Boneh International Conference on Learning Representations (ICLR), 2021 | 220 | 2021 |
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