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Vikash Sehwag
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Robustbench: a standardized adversarial robustness benchmark
F Croce, M Andriushchenko, V Sehwag, E Debenedetti, N Flammarion, ...
arXiv preprint arXiv:2010.09670, 2020
2622020
Ssd: A unified framework for self-supervised outlier detection
V Sehwag, M Chiang, P Mittal
arXiv preprint arXiv:2103.12051, 2021
1312021
Hydra: Pruning adversarially robust neural networks
V Sehwag, S Wang, P Mittal, S Jana
Advances in Neural Information Processing Systems 33, 19655-19666, 2020
1302020
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
1032020
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
67*2021
PatchGuard: A Provably Robust Defense against Adversarial Patches via Small Receptive Fields and Masking.
C Xiang, AN Bhagoji, V Sehwag, P Mittal
USENIX Security Symposium, 2237-2254, 2021
642021
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
522019
TV-PUF: a fast lightweight analog physical unclonable function
V Sehwag, T Saha
2016 IEEE International Symposium on Nanoelectronic and Information Systems …, 2016
302016
Towards compact and robust deep neural networks
V Sehwag, S Wang, P Mittal, S Jana
arXiv preprint arXiv:1906.06110, 2019
272019
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 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
142017
A critical evaluation of open-world machine learning
L Song, V Sehwag, AN Bhagoji, P Mittal
arXiv preprint arXiv:2007.04391, 2020
122020
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
102022
Extracting training data from diffusion models
N Carlini, J Hayes, M Nasr, M Jagielski, V Sehwag, F Tramèr, B Balle, ...
arXiv preprint arXiv:2301.13188, 2023
92023
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
82019
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
72018
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
52021
A light recipe to train robust vision transformers
E Debenedetti, V Sehwag, P Mittal
arXiv preprint arXiv:2209.07399, 2022
42022
Variation aware performance analysis of tfets for low-voltage computing
V Sehwag, S Maji, M Sharad
2016 IEEE International Symposium on Nanoelectronic and Information Systems …, 2016
32016
Just Rotate it: Deploying Backdoor Attacks via Rotation Transformation
T Wu, T Wang, V Sehwag, S Mahloujifar, P Mittal
arXiv preprint arXiv:2207.10825, 2022
22022
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Artículos 1–20