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2020 – today
- 2023
[i21]Tong Wu, Feiran Jia, Xiangyu Qi, Jiachen T. Wang, Vikash Sehwag, Saeed Mahloujifar, Prateek Mittal:
Uncovering Adversarial Risks of Test-Time Adaptation. CoRR abs/2301.12576 (2023)- [i20]Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, Eric Wallace:
Extracting Training Data from Diffusion Models. CoRR abs/2301.13188 (2023) - [i19]Sihui Dai, Saeed Mahloujifar, Chong Xiang, Vikash Sehwag, Pin-Yu Chen, Prateek Mittal:
MultiRobustBench: Benchmarking Robustness Against Multiple Attacks. CoRR abs/2302.10980 (2023) - 2022
- [c14]Tong Wu, Tianhao Wang, Vikash Sehwag, Saeed Mahloujifar, Prateek Mittal:
Just Rotate it: Deploying Backdoor Attacks via Rotation Transformation. AISec@CCS 2022: 91-102 - [c13]Vikash Sehwag, Caner Hazirbas, Albert Gordo, Firat Ozgenel, Cristian Canton-Ferrer:
Generating High Fidelity Data from Low-density Regions using Diffusion Models. CVPR 2022: 11482-11491 - [c12]Vikash Sehwag, Saeed Mahloujifar, Tinashe Handina, Sihui Dai, Chong Xiang, Mung Chiang, Prateek Mittal:
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness? ICLR 2022 - [c11]Christian Cianfarani, Arjun Nitin Bhagoji, Vikash Sehwag, Ben Y. Zhao, Heather Zheng, Prateek Mittal:
Understanding Robust Learning through the Lens of Representation Similarities. NeurIPS 2022 - [i18]Vikash Sehwag, Caner Hazirbas, Albert Gordo, Firat Ozgenel, Cristian Canton-Ferrer:
Generating High Fidelity Data from Low-density Regions using Diffusion Models. CoRR abs/2203.17260 (2022) - [i17]Christian Cianfarani, Arjun Nitin Bhagoji, Vikash Sehwag, Ben Y. Zhao, Prateek Mittal:
Understanding Robust Learning through the Lens of Representation Similarities. CoRR abs/2206.09868 (2022) - [i16]Tong Wu, Tianhao Wang, Vikash Sehwag, Saeed Mahloujifar, Prateek Mittal:
Just Rotate it: Deploying Backdoor Attacks via Rotation Transformation. CoRR abs/2207.10825 (2022) - [i15]Edoardo Debenedetti, Vikash Sehwag, Prateek Mittal:
A Light Recipe to Train Robust Vision Transformers. CoRR abs/2209.07399 (2022) - [i14]Ashwinee Panda, Xinyu Tang, Vikash Sehwag, Saeed Mahloujifar, Prateek Mittal:
DP-RAFT: A Differentially Private Recipe for Accelerated Fine-Tuning. CoRR abs/2212.04486 (2022) - 2021
- [j3]Prasad Nagabhushanamgari, Vikash Sehwag, Indrajit Chakrabarti, Santanu Chattopadhyay:
Embedding delay-based physical unclonable functions in networks-on-chip. IET Circuits Devices Syst. 15(1): 27-41 (2021) - [j2]Hung T. Nguyen
, Vikash Sehwag, Seyyedali Hosseinalipour, Christopher G. Brinton, Mung Chiang, H. Vincent Poor:
Fast-Convergent Federated Learning. IEEE J. Sel. Areas Commun. 39(1): 201-218 (2021) - [c10]Vikash Sehwag, Mung Chiang, Prateek Mittal:
SSD: A Unified Framework for Self-Supervised Outlier Detection. ICLR 2021 - [c9]Arjun Nitin Bhagoji, Daniel Cullina, Vikash Sehwag, Prateek Mittal:
Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries. ICML 2021: 863-873 - [c8]Vikash Sehwag, Jack W. Stokes, Cha Zhang:
Beyond $L_{p}$ Norms: Delving Deeper into Robustness to Physical Image Transformations. MILCOM 2021: 189-196 - [c7]Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, Matthias Hein:
RobustBench: a standardized adversarial robustness benchmark. NeurIPS Datasets and Benchmarks 2021 - [c6]Chong Xiang, Arjun Nitin Bhagoji, Vikash Sehwag, Prateek Mittal:
PatchGuard: A Provably Robust Defense against Adversarial Patches via Small Receptive Fields and Masking. USENIX Security Symposium 2021: 2237-2254 - [i13]Vikash Sehwag, Mung Chiang, Prateek Mittal:
SSD: A Unified Framework for Self-Supervised Outlier Detection. CoRR abs/2103.12051 (2021) - [i12]Arjun Nitin Bhagoji, Daniel Cullina, Vikash Sehwag, Prateek Mittal:
Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries. CoRR abs/2104.08382 (2021) - [i11]Vikash Sehwag, Saeed Mahloujifar, Tinashe Handina, Sihui Dai, Chong Xiang, Mung Chiang, Prateek Mittal:
Improving Adversarial Robustness Using Proxy Distributions. CoRR abs/2104.09425 (2021) - 2020
- [c5]Vikash Sehwag, Shiqi Wang, Prateek Mittal, Suman Jana:
HYDRA: Pruning Adversarially Robust Neural Networks. NeurIPS 2020 - [i10]Vikash Sehwag, Shiqi Wang, Prateek Mittal, Suman Jana:
On Pruning Adversarially Robust Neural Networks. CoRR abs/2002.10509 (2020) - [i9]Chong Xiang, Arjun Nitin Bhagoji, Vikash Sehwag, Prateek Mittal:
PatchGuard: Provable Defense against Adversarial Patches Using Masks on Small Receptive Fields. CoRR abs/2005.10884 (2020) - [i8]Vikash Sehwag, Rajvardhan Oak, Mung Chiang, Prateek Mittal:
Time for a Background Check! Uncovering the impact of Background Features on Deep Neural Networks. CoRR abs/2006.14077 (2020) - [i7]Liwei Song, Vikash Sehwag, Arjun Nitin Bhagoji, Prateek Mittal:
A Critical Evaluation of Open-World Machine Learning. CoRR abs/2007.04391 (2020) - [i6]Hung T. Nguyen, Vikash Sehwag, Seyyedali Hosseinalipour, Christopher G. Brinton, Mung Chiang, H. Vincent Poor:
Fast-Convergent Federated Learning. CoRR abs/2007.13137 (2020) - [i5]Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Nicolas Flammarion, Mung Chiang, Prateek Mittal, Matthias Hein:
RobustBench: a standardized adversarial robustness benchmark. CoRR abs/2010.09670 (2020)
2010 – 2019
- 2019
- [c4]Vikash Sehwag, Arjun Nitin Bhagoji, Liwei Song, Chawin Sitawarin, Daniel Cullina, Mung Chiang, Prateek Mittal:
Analyzing the Robustness of Open-World Machine Learning. AISec@CCS 2019: 105-116 - [i4]Vikash Sehwag, Arjun Nitin Bhagoji, Liwei Song, Chawin Sitawarin, Daniel Cullina, Mung Chiang, Prateek Mittal:
Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples. CoRR abs/1905.01726 (2019) - [i3]Vikash Sehwag, Shiqi Wang, Prateek Mittal, Suman Jana:
Towards Compact and Robust Deep Neural Networks. CoRR abs/1906.06110 (2019) - 2018
- [j1]Vikash Sehwag, N. Prasad, Indrajit Chakrabarti:
A Parallel Stochastic Number Generator With Bit Permutation Networks. IEEE Trans. Circuits Syst. II Express Briefs 65-II(2): 231-235 (2018) - [c3]Vikash Sehwag, Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Mung Chiang, Prateek Mittal:
Not All Pixels are Born Equal: An Analysis of Evasion Attacks under Locality Constraints. CCS 2018: 2285-2287 - 2016
- [c2]Vikash Sehwag, Saurav Maji, Mrigank Sharad:
Variation Aware Performance Analysis of TFETs for Low-Voltage Computing. iNIS 2016: 93-97 - [c1]Vikash Sehwag, Tanujay Saha:
TV-PUF: A Fast Lightweight Analog Physical Unclonable Function. iNIS 2016: 182-186 - [i2]Tanujay Saha, Vikash Sehwag:
TV-PUF : A Fast Lightweight Aging-Resistant Threshold Voltage PUF. IACR Cryptol. ePrint Arch. 2016: 774 (2016) - 2015
- [i1]Aranya Goswamy, Sagar Kumashi, Vikash Sehwag, Siddharth Singh, Manny Jain, Kaushik Roy, Mrigank Sharad:
Energy Efficient and High Performance Current-Mode Neural Network Circuit using Memristors and Digitally Assisted Analog CMOS Neurons. CoRR abs/1511.09085 (2015)
Coauthor Index
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last updated on 2023-05-13 03:59 CEST by the dblp team
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