| Improving adversarial robustness using proxy distributions V Sehwag, S Mahloujifar, T Handina, S Dai, C Xiang, M Chiang, P Mittal arXiv preprint arXiv:2104.09425 1, 2021 | 179* | 2021 |
| The curse of concentration in robust learning: Evasion and poisoning attacks from concentration of measure S Mahloujifar, DI Diochnos, M Mahmoody Proceedings of the AAAI Conference on Artificial Intelligence 33, 4536-4543, 2019 | 167 | 2019 |
| Sparsefed: Mitigating model poisoning attacks in federated learning with sparsification A Panda, S Mahloujifar, AN Bhagoji, S Chakraborty, P Mittal International Conference on Artificial Intelligence and Statistics, 7587-7624, 2022 | 104 | 2022 |
| Property inference from poisoning S Mahloujifar, E Ghosh, M Chase 2022 IEEE Symposium on Security and Privacy (SP), 1120-1137, 2022 | 102* | 2022 |
| Revisiting the assumption of latent separability for backdoor defenses X Qi, T Xie, Y Li, S Mahloujifar, P Mittal The eleventh international conference on learning representations, 2023 | 97 | 2023 |
| Is private learning possible with instance encoding? N Carlini, S Deng, S Garg, S Jha, S Mahloujifar, M Mahmoody, A Thakurta, ... 2021 IEEE Symposium on Security and Privacy (SP), 410-427, 2021 | 92 | 2021 |
| Adversarial risk and robustness: General definitions and implications for the uniform distribution D Diochnos, S Mahloujifar, M Mahmoody Advances in Neural Information Processing Systems 31, 2018 | 89 | 2018 |
| Mitigating membership inference attacks by {Self-Distillation} through a novel ensemble architecture X Tang, S Mahloujifar, L Song, V Shejwalkar, M Nasr, A Houmansadr, ... 31st USENIX Security Symposium (USENIX Security 22), 1433-1450, 2022 | 88 | 2022 |
| Universal Multi-Party Poisoning Attacks S Mahloujifar, M Mahmoody, A Mohammed International Conference on Machine Learning, 4274-4283, 2019 | 88* | 2019 |
| {PatchCleanser}: Certifiably robust defense against adversarial patches for any image classifier C Xiang, S Mahloujifar, P Mittal 31st USENIX Security Symposium (USENIX Security 22), 2065-2082, 2022 | 80 | 2022 |
| Model-targeted poisoning attacks with provable convergence F Suya, S Mahloujifar, A Suri, D Evans, Y Tian International Conference on Machine Learning, 10000-10010, 2021 | 52 | 2021 |
| Can Adversarially Robust Learning Leverage Computational Hardness? S Mahloujifar, M Mahmoody [edit] Saeed Mahloujifar, Mohammad Mahmoody ; Proceedings of the 30th …, 2019 | 46 | 2019 |
| Towards a proactive {ML} approach for detecting backdoor poison samples X Qi, T Xie, JT Wang, T Wu, S Mahloujifar, P Mittal 32nd USENIX Security Symposium (USENIX Security 23), 1685-1702, 2023 | 40 | 2023 |
| Membership inference on word embedding and beyond S Mahloujifar, HA Inan, M Chase, E Ghosh, M Hasegawa arXiv preprint arXiv:2106.11384, 2021 | 40 | 2021 |
| Learning under -Tampering Attacks S Mahloujifar, DI Diochnos, M Mahmoody Proceedings of Algorithmic Learning Theory, PMLR 83:572-596, 2018., 2018 | 35 | 2018 |
| Bounding training data reconstruction in dp-sgd J Hayes, B Balle, S Mahloujifar Advances in Neural Information Processing Systems 36, 2024 | 33 | 2024 |
| Parameterizing activation functions for adversarial robustness S Dai, S Mahloujifar, P Mittal 2022 IEEE Security and Privacy Workshops (SPW), 80-87, 2022 | 31 | 2022 |
| Blockwise p-Tampering Attacks on Cryptographic Primitives, Extractors, and Learners S Mahloujifar, M Mahmoody Theory of Cryptography: 15th International Conference, TCC 2017, Baltimore …, 2017 | 30 | 2017 |
| Just rotate it: Deploying backdoor attacks via rotation transformation T Wu, T Wang, V Sehwag, S Mahloujifar, P Mittal Proceedings of the 15th ACM Workshop on Artificial Intelligence and Security …, 2022 | 27 | 2022 |
| Publicly detectable watermarking for language models J Fairoze, S Garg, S Jha, S Mahloujifar, M Mahmoody, M Wang arXiv preprint arXiv:2310.18491, 2023 | 26 | 2023 |
Prateek MittalProfessor, Princeton UniversityVerifierad e-postadress på princeton.edu
