| Inverting gradients-how easy is it to break privacy in federated learning? J Geiping, H Bauermeister, H Dröge, M Moeller Advances in Neural Information Processing Systems 33, 16937-16947, 2020 | 503 | 2020 |
| Metapoison: Practical general-purpose clean-label data poisoning WR Huang, J Geiping, L Fowl, G Taylor, T Goldstein Advances in Neural Information Processing Systems 33, 12080-12091, 2020 | 107 | 2020 |
| Witches' brew: Industrial scale data poisoning via gradient matching J Geiping, L Fowl, WR Huang, W Czaja, G Taylor, M Moeller, T Goldstein Ninth International Conference on Learning Representations 2021, 2021 | 106 | 2021 |
| Strong data augmentation sanitizes poisoning and backdoor attacks without an accuracy tradeoff E Borgnia, V Cherepanova, L Fowl, A Ghiasi, J Geiping, M Goldblum, ... ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 56 | 2021 |
| Adversarial examples make strong poisons L Fowl, M Goldblum, P Chiang, J Geiping, W Czaja, T Goldstein Advances in Neural Information Processing Systems 34, 30339–30351, 2021 | 40 | 2021 |
| Cold diffusion: Inverting arbitrary image transforms without noise A Bansal, E Borgnia, HM Chu, JS Li, H Kazemi, F Huang, M Goldblum, ... arXiv preprint arXiv:2208.09392, 2022 | 39 | 2022 |
| What Doesn't Kill You Makes You Robust (er): Adversarial Training against Poisons and Backdoors J Geiping, L Fowl, G Somepalli, M Goldblum, M Moeller, T Goldstein ICLR 2021 Workshop on Security and Safety in Machine Learning Systems, 2021 | 36* | 2021 |
| Stochastic training is not necessary for generalization J Geiping, M Goldblum, PE Pope, M Moeller, T Goldstein The Tenth International Conference on Learning Representations, 2022 | 35 | 2022 |
| Truth or backpropaganda? An empirical investigation of deep learning theory M Goldblum, J Geiping, A Schwarzschild, M Moeller, T Goldstein Eighth International Conference on Learning Representations, 2020 | 30 | 2020 |
| Robbing the fed: Directly obtaining private data in federated learning with modified models L Fowl, J Geiping, W Czaja, M Goldblum, T Goldstein Tenth International Conference on Learning Representations, 2022 | 29 | 2022 |
| Preventing unauthorized use of proprietary data: Poisoning for secure dataset release L Fowl, P Chiang, M Goldblum, J Geiping, A Bansal, W Czaja, T Goldstein ICLR 2021 Workshop on Security and Safety in Machine Learning Systems, 2021 | 25 | 2021 |
| Dp-instahide: Provably defusing poisoning and backdoor attacks with differentially private data augmentations E Borgnia, J Geiping, V Cherepanova, L Fowl, A Gupta, A Ghiasi, ... ICLR 2021 Workshop on Security and Safety in Machine Learning Systems, 2021 | 22 | 2021 |
| Fishing for user data in large-batch federated learning via gradient magnification Y Wen, J Geiping, L Fowl, M Goldblum, T Goldstein Proceedings of the 39th International Conference on Machine Learning, 23668 …, 2022 | 16 | 2022 |
| Composite optimization by nonconvex majorization-minimization J Geiping, M Moeller SIAM Journal on Imaging Sciences 11 (4), 2494-2528, 2018 | 16 | 2018 |
| A watermark for large language models J Kirchenbauer, J Geiping, Y Wen, J Katz, I Miers, T Goldstein arXiv preprint arXiv:2301.10226, 2023 | 13 | 2023 |
| Witchcraft: Efficient PGD attacks with random step size PY Chiang, J Geiping, M Goldblum, T Goldstein, R Ni, S Reich, A Shafahi ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 9 | 2020 |
| Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models G Somepalli, V Singla, M Goldblum, J Geiping, T Goldstein arXiv preprint arXiv:2212.03860, 2022 | 7 | 2022 |
| Decepticons: Corrupted transformers breach privacy in federated learning for language models L Fowl, J Geiping, S Reich, Y Wen, W Czaja, M Goldblum, T Goldstein arXiv preprint arXiv:2201.12675, 2022 | 5 | 2022 |
| Autoregressive Perturbations for Data Poisoning P Sandoval-Segura, V Singla, J Geiping, M Goldblum, T Goldstein, ... Advances in Neural Information Processing Systems 34, 2022 | 5 | 2022 |
| Parametric majorization for data-driven energy minimization methods J Geiping, M Moeller Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 5 | 2019 |
Tom GoldsteinPerotto Associate Professor of Computer Science, University of MarylandVerified email at cs.umd.edu
