Vikash Sehwag
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
18
Venues
6
Active years
2018–2025
Best venue rank
A*
Where they publish
Papers
18 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | CVPR | CO-SPY: Combining Semantic and Pixel Features to Detect Synthetic Images by AI. | Siyuan Cheng, Lingjuan Lyu, Zhenting Wang, Xiangyu Zhang, Vikash Sehwag |
| 2025 | CVPR | Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget. | Vikash Sehwag, Xianghao Kong, Jingtao Li, Michael Spranger, Lingjuan Lyu |
| 2025 | CVPR | Argus: A Compact and Versatile Foundation Model for Vision. | Weiming Zhuang, Chen Chen, Zhizhong Li, Sina Sajadmanesh, Jingtao Li, Jiabo Huang, Vikash Sehwag, Vivek Sharma, Hirotaka Shinozaki, Felan Carlo Garcia, Yihao Zhan, Naohiro Adachi, Ryoji Eki, Michael Spranger, Peter Stone, Lingjuan Lyu |
| 2025 | ICML | Adapting to Evolving Adversaries with Regularized Continual Robust Training. | Sihui Dai, Christian Cianfarani, Vikash Sehwag, Prateek Mittal, Arjun Nitin Bhagoji |
| 2025 | ICML | How to Evaluate and Mitigate IP Infringement in Visual Generative AI? | Zhenting Wang, Chen Chen, Vikash Sehwag, Minzhou Pan, Lingjuan Lyu |
| 2025 | WWW | Self-Comparison for Dataset-Level Membership Inference in Large (Vision-)Language Model. | Jie Ren, Kangrui Chen, Chen Chen, Vikash Sehwag, Yue Xing, Jiliang Tang, Lingjuan Lyu |
| 2024 | ECCV | Finding Needles in a Haystack: A Black-Box Approach to Invisible Watermark Detection. | Minzhou Pan, Zhenting Wang, Xin Dong, Vikash Sehwag, Lingjuan Lyu, Xue Lin |
| 2024 | ICML | A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization. | Ashwinee Panda, Xinyu Tang, Saeed Mahloujifar, Vikash Sehwag, Prateek Mittal |
| 2024 | ICML | How to Trace Latent Generative Model Generated Images without Artificial Watermark? | Zhenting Wang, Vikash Sehwag, Chen Chen, Lingjuan Lyu, Dimitris N. Metaxas, Shiqing Ma |
| 2023 | ICML | MultiRobustBench: Benchmarking Robustness Against Multiple Attacks. | Sihui Dai, Saeed Mahloujifar, Chong Xiang, Vikash Sehwag, Pin-Yu Chen, Prateek Mittal |
| 2023 | ICML | Uncovering Adversarial Risks of Test-Time Adaptation. | Tong Wu, Feiran Jia, Xiangyu Qi, Jiachen T. Wang, Vikash Sehwag, Saeed Mahloujifar, Prateek Mittal |
| 2022 | CCS | Just Rotate it: Deploying Backdoor Attacks via Rotation Transformation. | Tong Wu, Tianhao Wang, Vikash Sehwag, Saeed Mahloujifar, Prateek Mittal |
| 2022 | CVPR | Generating High Fidelity Data from Low-density Regions using Diffusion Models. | Vikash Sehwag, Caner Hazirbas, Albert Gordo, Firat Ozgenel, Cristian Canton-Ferrer |
| 2022 | ICLR | Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness? | Vikash Sehwag, Saeed Mahloujifar, Tinashe Handina, Sihui Dai, Chong Xiang, Mung Chiang, Prateek Mittal |
| 2021 | ICLR | SSD: A Unified Framework for Self-Supervised Outlier Detection. | Vikash Sehwag, Mung Chiang, Prateek Mittal |
| 2021 | ICML | Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries. | Arjun Nitin Bhagoji, Daniel Cullina, Vikash Sehwag, Prateek Mittal |
| 2019 | CCS | Analyzing the Robustness of Open-World Machine Learning. | Vikash Sehwag, Arjun Nitin Bhagoji, Liwei Song, Chawin Sitawarin, Daniel Cullina, Mung Chiang, Prateek Mittal |
| 2018 | CCS | Not All Pixels are Born Equal: An Analysis of Evasion Attacks under Locality Constraints. | Vikash Sehwag, Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Mung Chiang, Prateek Mittal |