| Robustbench: a standardized adversarial robustness benchmark F Croce, M Andriushchenko, V Sehwag, E Debenedetti, N Flammarion, ... arXiv preprint arXiv:2010.09670, 2020 | 436 | 2020 |
| Ssd: A unified framework for self-supervised outlier detection V Sehwag, M Chiang, P Mittal arXiv preprint arXiv:2103.12051, 2021 | 228 | 2021 |
| Hydra: Pruning adversarially robust neural networks V Sehwag, S Wang, P Mittal, S Jana Advances in Neural Information Processing Systems 33, 19655-19666, 2020 | 173 | 2020 |
| 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 | 155 | 2023 |
| Fast-convergent federated learning HT Nguyen, V Sehwag, S Hosseinalipour, CG Brinton, M Chiang, ... IEEE Journal on Selected Areas in Communications 39 (1), 201-218, 2020 | 154 | 2020 |
| Robust learning meets generative models: Can proxy distributions improve adversarial robustness? V Sehwag, S Mahloujifar, T Handina, S Dai, C Xiang, M Chiang, P Mittal arXiv preprint arXiv:2104.09425, 2021 | 115* | 2021 |
| {PatchGuard}: A provably robust defense against adversarial patches via small receptive fields and masking C Xiang, AN Bhagoji, V Sehwag, P Mittal 30th USENIX Security Symposium (USENIX Security 21), 2237-2254, 2021 | 114 | 2021 |
| Analyzing the robustness of open-world machine learning V Sehwag, AN Bhagoji, L Song, C Sitawarin, D Cullina, M Chiang, P Mittal Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security …, 2019 | 68 | 2019 |
| TV-PUF: A fast lightweight analog physical unclonable function V Sehwag, T Saha 2016 IEEE International Symposium on Nanoelectronic and Information Systems …, 2016 | 36 | 2016 |
| Towards compact and robust deep neural networks V Sehwag, S Wang, P Mittal, S Jana arXiv preprint arXiv:1906.06110, 2019 | 35 | 2019 |
| Generating high fidelity data from low-density regions using diffusion models V Sehwag, C Hazirbas, A Gordo, F Ozgenel, C Canton Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 31 | 2022 |
| Time for a background check! uncovering the impact of background features on deep neural networks V Sehwag, R Oak, M Chiang, P Mittal arXiv preprint arXiv:2006.14077, 2020 | 21 | 2020 |
| A light recipe to train robust vision transformers E Debenedetti, V Sehwag, P Mittal 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 225-253, 2023 | 18 | 2023 |
| A parallel stochastic number generator with bit permutation networks V Sehwag, N Prasad, I Chakrabarti IEEE Transactions on Circuits and Systems II: Express Briefs 65 (2), 231-235, 2017 | 15 | 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 | 14 | 2022 |
| A critical evaluation of open-world machine learning L Song, V Sehwag, AN Bhagoji, P Mittal arXiv preprint arXiv:2007.04391, 2020 | 14 | 2020 |
| Better the devil you know: An analysis of evasion attacks using out-of-distribution adversarial examples V Sehwag, AN Bhagoji, L Song, C Sitawarin, D Cullina, M Chiang, P Mittal arXiv preprint arXiv:1905.01726, 2019 | 12 | 2019 |
| Dp-raft: A differentially private recipe for accelerated fine-tuning A Panda, X Tang, V Sehwag, S Mahloujifar, P Mittal arXiv preprint arXiv:2212.04486, 2022 | 9 | 2022 |
| Not all pixels are born equal: An analysis of evasion attacks under locality constraints V Sehwag, C Sitawarin, AN Bhagoji, A Mosenia, M Chiang, P Mittal Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications …, 2018 | 7 | 2018 |
| Lower bounds on cross-entropy loss in the presence of test-time adversaries AN Bhagoji, D Cullina, V Sehwag, P Mittal International Conference on Machine Learning, 863-873, 2021 | 6 | 2021 |
Prateek MittalProfessor, Princeton UniversityVerified email at princeton.edu
