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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, ...
Conference on Neural Information Processing Systems (NeurIPS) 2021 …, 2020
7552020
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
5502023
Ssd: A unified framework for self-supervised outlier detection
V Sehwag, M Chiang, P Mittal
International Conference on Learning Representations (ICLR), 2021, 2021
3512021
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
2402020
Hydra: Pruning adversarially robust neural networks
V Sehwag, S Wang, P Mittal, S Jana
Advances in Neural Information Processing Systems 33, 19655-19666, 2020
2292020
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
International Conference on Learning Representations (ICLR), 2022, 2021
179*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
1782021
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
982019
Jailbreakbench: An open robustness benchmark for jailbreaking large language models
P Chao, E Debenedetti, A Robey, M Andriushchenko, F Croce, V Sehwag, ...
arXiv preprint arXiv:2404.01318, 2024
762024
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
622022
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
592023
Towards compact and robust deep neural networks
V Sehwag, S Wang, P Mittal, S Jana
arXiv preprint arXiv:1906.06110, 2019
392019
TV-PUF: A fast lightweight analog physical unclonable function
V Sehwag, T Saha
2016 IEEE International Symposium on Nanoelectronic and Information Systems …, 2016
392016
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
272022
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
202020
A critical evaluation of open-world machine learning
L Song, V Sehwag, AN Bhagoji, P Mittal
arXiv preprint arXiv:2007.04391, 2020
182020
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
182017
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
162019
Dp-raft: A differentially private recipe for accelerated fine-tuning
A Panda, X Tang, V Sehwag, S Mahloujifar, P Mittal
arXiv e-prints, 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
122022
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