Energy Efficient and High Performance Current-Mode Neural Network Circuit using Memristors and Digitally Assisted Analog CMOS Neurons. - Aranya Goswamy; Sagar Kumashi; Vikash Sehwag; Siddharth Singh; Manny Jain; Kaushik Roy 001; Mrigank Sharad (CoRR, 2015)

Variation Aware Performance Analysis of TFETs for Low-Voltage Computing. - Vikash Sehwag; Saurav Maji; Mrigank Sharad (iNIS, 2016)

TV-PUF: A Fast Lightweight Analog Physical Unclonable Function. - Vikash Sehwag; Tanujay Saha (iNIS, 2016)

TV-PUF : A Fast Lightweight Aging-Resistant Threshold Voltage PUF. - Tanujay Saha; Vikash Sehwag (IACR Cryptol. ePrint Arch., 2016)

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 (CCS, 2018)

A Parallel Stochastic Number Generator With Bit Permutation Networks. - Vikash Sehwag; N. Prasad 001; Indrajit Chakrabarti (IEEE Trans. Circuits Syst. II Express Briefs, 2018)

Analyzing the Robustness of Open-World Machine Learning. - Vikash Sehwag; Arjun Nitin Bhagoji; Liwei Song; Chawin Sitawarin; Daniel Cullina; Mung Chiang; Prateek Mittal (AISec@CCS, 2019)

Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples. - Vikash Sehwag; Arjun Nitin Bhagoji; Liwei Song; Chawin Sitawarin; Daniel Cullina; Mung Chiang; Prateek Mittal (CoRR, 2019)

Towards Compact and Robust Deep Neural Networks. - Vikash Sehwag; Shiqi Wang 002; Prateek Mittal; Suman Jana (CoRR, 2019)

HYDRA: Pruning Adversarially Robust Neural Networks. - Vikash Sehwag; Shiqi Wang 002; Prateek Mittal; Suman Jana (NeurIPS, 2020)

On Pruning Adversarially Robust Neural Networks. - Vikash Sehwag; Shiqi Wang 002; Prateek Mittal; Suman Jana (CoRR, 2020)

PatchGuard: Provable Defense against Adversarial Patches Using Masks on Small Receptive Fields. - Chong Xiang 001; Arjun Nitin Bhagoji; Vikash Sehwag; Prateek Mittal (CoRR, 2020)

Time for a Background Check! Uncovering the impact of Background Features on Deep Neural Networks. - Vikash Sehwag; Rajvardhan Oak; Mung Chiang; Prateek Mittal (CoRR, 2020)

A Critical Evaluation of Open-World Machine Learning. - Liwei Song; Vikash Sehwag; Arjun Nitin Bhagoji; Prateek Mittal (CoRR, 2020)

RobustBench: a standardized adversarial robustness benchmark. - Francesco Croce; Maksym Andriushchenko; Vikash Sehwag; Nicolas Flammarion; Mung Chiang; Prateek Mittal; Matthias Hein 001 (CoRR, 2020)

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

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

Beyond $L_{p}$ Norms: Delving Deeper into Robustness to Physical Image Transformations. - Vikash Sehwag; Jack W. Stokes; Cha Zhang (MILCOM, 2021)

RobustBench: a standardized adversarial robustness benchmark. - Francesco Croce; Maksym Andriushchenko; Vikash Sehwag; Edoardo Debenedetti; Nicolas Flammarion; Mung Chiang; Prateek Mittal; Matthias Hein 001 (NeurIPS Datasets and Benchmarks, 2021)

PatchGuard: A Provably Robust Defense against Adversarial Patches via Small Receptive Fields and Masking. - Chong Xiang 001; Arjun Nitin Bhagoji; Vikash Sehwag; Prateek Mittal (USENIX Security Symposium, 2021)

Improving Adversarial Robustness Using Proxy Distributions. - Vikash Sehwag; Saeed Mahloujifar; Tinashe Handina; Sihui Dai; Chong Xiang 001; Mung Chiang; Prateek Mittal (CoRR, 2021)

Embedding delay-based physical unclonable functions in networks-on-chip. - Prasad Nagabhushanamgari; Vikash Sehwag; Indrajit Chakrabarti; Santanu Chattopadhyay (IET Circuits Devices Syst., 2021)

Fast-Convergent Federated Learning. - Hung T. Nguyen 003; Vikash Sehwag; Seyyedali Hosseinalipour; Christopher G. Brinton; Mung Chiang; H. Vincent Poor (IEEE J. Sel. Areas Commun., 2021)

Just Rotate it: Deploying Backdoor Attacks via Rotation Transformation. - Tong Wu; Tianhao Wang 021; Vikash Sehwag; Saeed Mahloujifar; Prateek Mittal (AISec@CCS, 2022)

Generating High Fidelity Data from Low-density Regions using Diffusion Models. - Vikash Sehwag; Caner Hazirbas; Albert Gordo; Firat Ozgenel; Cristian Canton-Ferrer (CVPR, 2022)

Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness? - Vikash Sehwag; Saeed Mahloujifar; Tinashe Handina; Sihui Dai; Chong Xiang 001; Mung Chiang; Prateek Mittal (ICLR, 2022)

Understanding Robust Learning through the Lens of Representation Similarities. - Christian Cianfarani; Arjun Nitin Bhagoji; Vikash Sehwag; Ben Y. Zhao; Heather Zheng; Prateek Mittal (NeurIPS, 2022)

Understanding Robust Learning through the Lens of Representation Similarities. - Christian Cianfarani; Arjun Nitin Bhagoji; Vikash Sehwag; Ben Y. Zhao; Prateek Mittal (CoRR, 2022)

DP-RAFT: A Differentially Private Recipe for Accelerated Fine-Tuning. - Ashwinee Panda; Xinyu Tang 003; Vikash Sehwag; Saeed Mahloujifar; Prateek Mittal (CoRR, 2022)

MultiRobustBench: Benchmarking Robustness Against Multiple Attacks. - Sihui Dai; Saeed Mahloujifar; Chong Xiang 001; 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 (ICML, 2023)

A Light Recipe to Train Robust Vision Transformers. - Edoardo Debenedetti; Vikash Sehwag; Prateek Mittal (SaTML, 2023)

Extracting Training Data from Diffusion Models. - Nicholas Carlini; Jamie Hayes; Milad Nasr; Matthew Jagielski; Vikash Sehwag; Florian Tramèr; Borja Balle; Daphne Ippolito; Eric Wallace (USENIX Security Symposium, 2023)

Scaling Compute Is Not All You Need for Adversarial Robustness. - Edoardo Debenedetti; Zishen Wan; Maksym Andriushchenko; Vikash Sehwag; Kshitij Bhardwaj; Bhavya Kailkhura (CoRR, 2023)

Promises and Pitfalls of Generative AI: An AI-Safety Centric Approach - Vikash Sehwag (2023)

Finding Needles in a Haystack: A Black-Box Approach to Invisible Watermark Detection. - Minzhou Pan; Zhenting Wang; Xin Dong 009; Vikash Sehwag; Lingjuan Lyu; Xue Lin 001 (ECCV (33), 2024)

A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization. - Ashwinee Panda; Xinyu Tang 003; Saeed Mahloujifar; Vikash Sehwag; Prateek Mittal (ICML, 2024)

How to Trace Latent Generative Model Generated Images without Artificial Watermark? - Zhenting Wang; Vikash Sehwag; Chen Chen 043; Lingjuan Lyu; Dimitris N. Metaxas; Shiqing Ma (ICML, 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 001 (NeurIPS, 2024)

AI Risk Management Should Incorporate Both Safety and Security. - Xiangyu Qi; Yangsibo Huang; Yi Zeng 005; Edoardo Debenedetti; Jonas Geiping; Luxi He; Kaixuan Huang; Udari Madhushani; Vikash Sehwag; Weijia Shi; Boyi Wei; Tinghao Xie; et al. (CoRR, 2024)

Evaluating and Mitigating IP Infringement in Visual Generative AI. - Zhenting Wang; Chen Chen 043; Vikash Sehwag; Minzhou Pan; Lingjuan Lyu (CoRR, 2024)

EnTruth: Enhancing the Traceability of Unauthorized Dataset Usage in Text-to-image Diffusion Models with Minimal and Robust Alterations. - Jie Ren 019; Yingqian Cui; Chen Chen 043; Vikash Sehwag; Yue Xing 002; Jiliang Tang; Lingjuan Lyu (CoRR, 2024)

Self-Comparison for Dataset-Level Membership Inference in Large (Vision-)Language Models. - Jie Ren 019; Kangrui Chen; Chen Chen 043; Vikash Sehwag; Yue Xing 002; Jiliang Tang; Lingjuan Lyu (CoRR, 2024)

Masked Differential Privacy. - David Schneider 006; Sina Sajadmanesh; Vikash Sehwag; M. Saquib Sarfraz; Rainer Stiefelhagen; Lingjuan Lyu; Vivek Sharma 001 (CoRR, 2024)

CO-SPY: Combining Semantic and Pixel Features to Detect Synthetic Images by AI. - Siyuan Cheng 005; Lingjuan Lyu; Zhenting Wang; Xiangyu Zhang 001; Vikash Sehwag (CVPR, 2025)

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

Argus: A Compact and Versatile Foundation Model for Vision. - Weiming Zhuang; Chen Chen 043; Zhizhong Li; Sina Sajadmanesh; Jingtao Li; Jiabo Huang; Vikash Sehwag; Vivek Sharma 001; Hirotaka Shinozaki; Felan Carlo Garcia; Yihao Zhan; Naohiro Adachi; et al. (CVPR, 2025)

Self-Comparison for Dataset-Level Membership Inference in Large (Vision-)Language Model. - Jie Ren 019; Kangrui Chen; Chen Chen 043; Vikash Sehwag; Yue Xing 002; Jiliang Tang; Lingjuan Lyu (WWW, 2025)

Differentially Private Image Classification by Learning Priors from Random Processes. - Xinyu Tang 003; Ashwinee Panda; Vikash Sehwag; Prateek Mittal (J. Priv. Confidentiality, 2025)

Adapting to Evolving Adversaries with Regularized Continual Robust Training. - Sihui Dai; Christian Cianfarani; Arjun Nitin Bhagoji; Vikash Sehwag; Prateek Mittal (CoRR, 2025)

Does More Inference-Time Compute Really Help Robustness? - Tong Wu; Chong Xiang 001; Jiachen T. Wang; Weichen Yu; Chawin Sitawarin; Vikash Sehwag; Prateek Mittal (CoRR, 2025)