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Vikash Sehwag
Vikash Sehwag
Sony AI; Princeton University
Dirección de correo verificada de princeton.edu - Página principal
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Año
Robustbench: a standardized adversarial robustness benchmark
F Croce, M Andriushchenko, V Sehwag, E Debenedetti, N Flammarion, ...
arXiv preprint arXiv:2010.09670, 2020
5452020
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
2822023
Ssd: A unified framework for self-supervised outlier detection
V Sehwag, M Chiang, P Mittal
arXiv preprint arXiv:2103.12051, 2021
2792021
Hydra: Pruning adversarially robust neural networks
V Sehwag, S Wang, P Mittal, S Jana
Advances in Neural Information Processing Systems 33, 19655-19666, 2020
1962020
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
1832020
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
145*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
1372021
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
802019
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
382022
TV-PUF: A fast lightweight analog physical unclonable function
V Sehwag, T Saha
2016 IEEE International Symposium on Nanoelectronic and Information Systems …, 2016
382016
Towards compact and robust deep neural networks
V Sehwag, S Wang, P Mittal, S Jana
arXiv preprint arXiv:1906.06110, 2019
362019
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
322023
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
212022
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
172020
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
172017
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
152019
A critical evaluation of open-world machine learning
L Song, V Sehwag, AN Bhagoji, P Mittal
arXiv preprint arXiv:2007.04391, 2020
142020
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
132022
Understanding robust learning through the lens of representation similarities
C Cianfarani, AN Bhagoji, V Sehwag, B Zhao, H Zheng, P Mittal
Advances in Neural Information Processing Systems 35, 34912-34925, 2022
92022
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
82018
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