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Jonas Geiping
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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
3432020
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
752020
Witches' brew: Industrial scale data poisoning via gradient matching
J Geiping, L Fowl, WR Huang, W Czaja, G Taylor, M Moeller, T Goldstein
arXiv preprint arXiv:2009.02276, 2020
682020
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
342021
Truth or backpropaganda? An empirical investigation of deep learning theory
M Goldblum, J Geiping, A Schwarzschild, M Moeller, T Goldstein
arXiv preprint arXiv:1910.00359, 2019
252019
Adversarial examples make strong poisons
L Fowl, M Goldblum, P Chiang, J Geiping, W Czaja, T Goldstein
arXiv preprint arXiv:2106.10807, 2021
222021
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
arXiv preprint arXiv:2102.13624, 2021
192021
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, ...
arXiv preprint arXiv:2103.02079, 2021
162021
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
arXiv preprint arXiv:2103.02683, 2021
162021
Stochastic training is not necessary for generalization
J Geiping, M Goldblum, PE Pope, M Moeller, T Goldstein
arXiv preprint arXiv:2109.14119, 2021
152021
Composite optimization by nonconvex majorization-minimization
J Geiping, M Moeller
SIAM Journal on Imaging Sciences 11 (4), 2494-2528, 2018
142018
Robbing the fed: Directly obtaining private data in federated learning with modified models
L Fowl, J Geiping, W Czaja, M Goldblum, T Goldstein
arXiv preprint arXiv:2110.13057, 2021
102021
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
82020
Parametric majorization for data-driven energy minimization methods
J Geiping, M Moeller
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
52019
Comparison of topology-preserving segmentation methods and application to mitotic cell tracking
JA Geiping
Bachelor of Science thesis, Dept. Math. Comput. Sci., Westfälische Wilhelms …, 2014
52014
Fishing for user data in large-batch federated learning via gradient magnification
Y Wen, J Geiping, L Fowl, M Goldblum, T Goldstein
arXiv preprint arXiv:2202.00580, 2022
42022
Inverting gradientsśhow easy is it to break privacy in federated learning
J Geiping, H Bauermeister, H Dröge, M Moeller
arXiv preprint arXiv:2003.14053, 2020
42020
Piecewise rigid scene flow with implicit motion segmentation
A Görlitz, J Geiping, A Kolb
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019
42019
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
32022
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
22022
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