Sehwag, Vikash

23 publications

ICML 2025 Adapting to Evolving Adversaries with Regularized Continual Robust Training Sihui Dai, Christian Cianfarani, Vikash Sehwag, Prateek Mittal, Arjun Bhagoji

CVPR 2025 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

CVPR 2025 CO-SPY: Combining Semantic and Pixel Features to Detect Synthetic Images by AI Siyuan Cheng, Lingjuan Lyu, Zhenting Wang, Xiangyu Zhang, Vikash Sehwag

NeurIPS 2025 FineGRAIN: Evaluating Failure Modes of Text-to-Image Models with Vision Language Model Judges Kevin David Hayes, Micah Goldblum, Vikash Sehwag, Gowthami Somepalli, Ashwinee Panda, Tom Goldstein

ICML 2025 How to Evaluate and Mitigate IP Infringement in Visual Generative AI? Zhenting Wang, Chen Chen, Vikash Sehwag, Minzhou Pan, Lingjuan Lyu

TMLR 2025 Reliable and Responsible Foundation Models Xinyu Yang, Junlin Han, Rishi Bommasani, Jinqi Luo, Wenjie Qu, Wangchunshu Zhou, Adel Bibi, Xiyao Wang, Jaehong Yoon, Elias Stengel-Eskin, Shengbang Tong, Lingfeng Shen, Rafael Rafailov, Runjia Li, Zhaoyang Wang, Yiyang Zhou, Chenhang Cui, Yu Wang, Wenhao Zheng, Huichi Zhou, Jindong Gu, Zhaorun Chen, Peng Xia, Tony Lee, Thomas P Zollo, Vikash Sehwag, Jixuan Leng, Jiuhai Chen, Yuxin Wen, Huan Zhang, Zhun Deng, Linjun Zhang, Pavel Izmailov, Pang Wei Koh, Yulia Tsvetkov, Andrew Gordon Wilson, Jiaheng Zhang, James Zou, Cihang Xie, Hao Wang, Philip Torr, Julian McAuley, David Alvarez-Melis, Florian Tramèr, Kaidi Xu, Suman Jana, Chris Callison-Burch, Rene Vidal, Filippos Kokkinos, Mohit Bansal, Beidi Chen, Huaxiu Yao

CVPR 2025 Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget Vikash Sehwag, Xianghao Kong, Jingtao Li, Michael Spranger, Lingjuan Lyu

ICML 2024 A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization Ashwinee Panda, Xinyu Tang, Saeed Mahloujifar, Vikash Sehwag, Prateek Mittal

ECCV 2024 Finding a Needle in a Haystack: A Black-Box Approach to Invisible Watermark Detection Minzhou Pan, Zhenting Wang, Xin Dong, Vikash Sehwag, Lingjuan Lyu, Xue Lin

ICML 2024 How to Trace Latent Generative Model Generated Images Without Artificial Watermark? Zhenting Wang, Vikash Sehwag, Chen Chen, Lingjuan Lyu, Dimitris N. Metaxas, Shiqing Ma

NeurIPS 2024 JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko, Francesco Croce, Vikash Sehwag, Edgar Dobriban, Nicolas Flammarion, George J. Pappas, Florian Tramèr, Hamed Hassani, Eric Wong

ICMLW 2024 JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko, Francesco Croce, Vikash Sehwag, Edgar Dobriban, Nicolas Flammarion, George J. Pappas, Florian Tramèr, Hamed Hassani, Eric Wong

ICLRW 2024 Scaling Compute Is Not All You Need for Adversarial Robustness Edoardo Debenedetti, Zishen Wan, Maksym Andriushchenko, Vikash Sehwag, Kshitij Bhardwaj, Bhavya Kailkhura

ICMLW 2023 Differentially Private Generation of High Fidelity Samples from Diffusion Models Vikash Sehwag, Ashwinee Panda, Ashwini Pokle, Xinyu Tang, Saeed Mahloujifar, Mung Chiang, J Zico Kolter, Prateek Mittal

NeurIPS 2023 Differentially Private Image Classification by Learning Priors from Random Processes Xinyu Tang, Ashwinee Panda, Vikash Sehwag, Prateek Mittal

ICML 2023 MultiRobustBench: Benchmarking Robustness Against Multiple Attacks Sihui Dai, Saeed Mahloujifar, Chong Xiang, Vikash Sehwag, Pin-Yu Chen, Prateek Mittal

ICML 2023 Uncovering Adversarial Risks of Test-Time Adaptation Tong Wu, Feiran Jia, Xiangyu Qi, Jiachen T. Wang, Vikash Sehwag, Saeed Mahloujifar, Prateek Mittal

CVPR 2022 Generating High Fidelity Data from Low-Density Regions Using Diffusion Models Vikash Sehwag, Caner Hazirbas, Albert Gordo, Firat Ozgenel, Cristian Canton

ICLR 2022 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

NeurIPS 2022 Understanding Robust Learning Through the Lens of Representation Similarities Christian Cianfarani, Arjun Nitin Bhagoji, Vikash Sehwag, Ben Zhao, Heather Zheng, Prateek Mittal

ICML 2021 Lower Bounds on Cross-Entropy Loss in the Presence of Test-Time Adversaries Arjun Nitin Bhagoji, Daniel Cullina, Vikash Sehwag, Prateek Mittal

ICLR 2021 SSD: A Unified Framework for Self-Supervised Outlier Detection Vikash Sehwag, Mung Chiang, Prateek Mittal

NeurIPS 2020 HYDRA: Pruning Adversarially Robust Neural Networks Vikash Sehwag, Shiqi Wang, Prateek Mittal, Suman Jana