Vikash Sehwag

According to our database, Vikash Sehwag authored at least 46 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Masked Differential Privacy.

David Schneider, Sina Sajadmanesh, Vikash Sehwag, M. Saquib Sarfraz, Rainer Stiefelhagen, Lingjuan Lyu, Vivek Sharma

CoRR, 2024

Self-Comparison for Dataset-Level Membership Inference in Large (Vision-)Language Models.

Jie Ren, Kangrui Chen, Chen Chen, Vikash Sehwag, Yue Xing, Jiliang Tang, Lingjuan Lyu

CoRR, 2024

Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget.

Vikash Sehwag, Xianghao Kong, Jingtao Li, Michael Spranger, 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, Yingqian Cui, Chen Chen, Vikash Sehwag, Yue Xing, Jiliang Tang, Lingjuan Lyu

CoRR, 2024

Evaluating and Mitigating IP Infringement in Visual Generative AI.

Zhenting Wang, Chen Chen, Vikash Sehwag, Minzhou Pan, Lingjuan Lyu

CoRR, 2024

AI Risk Management Should Incorporate Both Safety and Security.

Xiangyu Qi, Yangsibo Huang, Yi Zeng, Edoardo Debenedetti, Jonas Geiping, Luxi He, Kaixuan Huang, Udari Madhushani, Vikash Sehwag, Weijia Shi, Boyi Wei, Tinghao Xie, Danqi Chen, Pin-Yu Chen, Jeffrey Ding, Ruoxi Jia, Jiaqi Ma, Arvind Narayanan, Weijie J. Su, Mengdi Wang, Chaowei Xiao, Bo Li, Dawn Song, Peter Henderson, Prateek Mittal

CoRR, 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

CoRR, 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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization.

Ashwinee Panda, Xinyu Tang, Saeed Mahloujifar, Vikash Sehwag, Prateek Mittal

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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

Proceedings of the Computer Vision - ECCV 2024, 2024

2023

Promises and Pitfalls of Generative AI: An AI-Safety Centric Approach

Vikash Sehwag

PhD thesis, 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

Extracting Training Data from Diffusion Models.

Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, Eric Wallace

Proceedings of the 32nd USENIX Security Symposium, 2023

A Light Recipe to Train Robust Vision Transformers.

Edoardo Debenedetti, Vikash Sehwag, Prateek Mittal

Proceedings of the 2023 IEEE Conference on Secure and Trustworthy Machine Learning, 2023

Differentially Private Image Classification by Learning Priors from Random Processes.

Xinyu Tang, Ashwinee Panda, Vikash Sehwag, Prateek Mittal

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Uncovering Adversarial Risks of Test-Time Adaptation.

Tong Wu, Feiran Jia, Xiangyu Qi, Jiachen T. Wang, Vikash Sehwag, Saeed Mahloujifar, Prateek Mittal

Proceedings of the International Conference on Machine Learning, 2023

MultiRobustBench: Benchmarking Robustness Against Multiple Attacks.

Sihui Dai, Saeed Mahloujifar, Chong Xiang, Vikash Sehwag, Pin-Yu Chen, Prateek Mittal

Proceedings of the International Conference on Machine Learning, 2023

2022

DP-RAFT: A Differentially Private Recipe for Accelerated Fine-Tuning.

Ashwinee Panda, Xinyu Tang, Vikash Sehwag, Saeed Mahloujifar, Prateek Mittal

CoRR, 2022

Understanding Robust Learning through the Lens of Representation Similarities.

Christian Cianfarani, Arjun Nitin Bhagoji, Vikash Sehwag, Ben Y. Zhao, Prateek Mittal

CoRR, 2022

Understanding Robust Learning through the Lens of Representation Similarities.

Christian Cianfarani, Arjun Nitin Bhagoji, Vikash Sehwag, Ben Y. Zhao, Heather Zheng, Prateek Mittal

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 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

Proceedings of the Tenth International Conference on Learning Representations, 2022

Generating High Fidelity Data from Low-density Regions using Diffusion Models.

Vikash Sehwag, Caner Hazirbas, Albert Gordo, Firat Ozgenel, Cristian Canton-Ferrer

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Just Rotate it: Deploying Backdoor Attacks via Rotation Transformation.

Tong Wu, Tianhao Wang, Vikash Sehwag, Saeed Mahloujifar, Prateek Mittal

Proceedings of the 15th ACM Workshop on Artificial Intelligence and Security, 2022

2021

Fast-Convergent Federated Learning.

Hung T. Nguyen, Vikash Sehwag, Seyyedali Hosseinalipour, Christopher G. Brinton, Mung Chiang, H. Vincent Poor

IEEE J. Sel. Areas Commun., 2021

Embedding delay-based physical unclonable functions in networks-on-chip.

Prasad Nagabhushanamgari, Vikash Sehwag, Indrajit Chakrabarti, Santanu Chattopadhyay

IET Circuits Devices Syst., 2021

Improving Adversarial Robustness Using Proxy Distributions.

Vikash Sehwag, Saeed Mahloujifar, Tinashe Handina, Sihui Dai, Chong Xiang, Mung Chiang, Prateek Mittal

CoRR, 2021

PatchGuard: A Provably Robust Defense against Adversarial Patches via Small Receptive Fields and Masking.

Chong Xiang, Arjun Nitin Bhagoji, Vikash Sehwag, Prateek Mittal

Proceedings of the 30th USENIX Security Symposium, 2021

RobustBench: a standardized adversarial robustness benchmark.

Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, Matthias Hein

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Beyond $L_{p}$ Norms: Delving Deeper into Robustness to Physical Image Transformations.

Vikash Sehwag, Jack W. Stokes, Cha Zhang

Proceedings of the 2021 IEEE Military Communications Conference, 2021

Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries.

Arjun Nitin Bhagoji, Daniel Cullina, Vikash Sehwag, Prateek Mittal

Proceedings of the 38th International Conference on Machine Learning, 2021

SSD: A Unified Framework for Self-Supervised Outlier Detection.

Vikash Sehwag, Mung Chiang, Prateek Mittal

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

RobustBench: a standardized adversarial robustness benchmark.

Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Nicolas Flammarion, Mung Chiang, Prateek Mittal, Matthias Hein

CoRR, 2020

A Critical Evaluation of Open-World Machine Learning.

Liwei Song, Vikash Sehwag, Arjun Nitin Bhagoji, 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

PatchGuard: Provable Defense against Adversarial Patches Using Masks on Small Receptive Fields.

Chong Xiang, Arjun Nitin Bhagoji, Vikash Sehwag, Prateek Mittal

CoRR, 2020

On Pruning Adversarially Robust Neural Networks.

Vikash Sehwag, Shiqi Wang, Prateek Mittal, Suman Jana

CoRR, 2020

HYDRA: Pruning Adversarially Robust Neural Networks.

Vikash Sehwag, Shiqi Wang, Prateek Mittal, Suman Jana

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Towards Compact and Robust Deep Neural Networks.

Vikash Sehwag, Shiqi Wang, Prateek Mittal, Suman Jana

CoRR, 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

Analyzing the Robustness of Open-World Machine Learning.

Vikash Sehwag, Arjun Nitin Bhagoji, Liwei Song, Chawin Sitawarin, Daniel Cullina, Mung Chiang, Prateek Mittal

Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security, 2019

2018

A Parallel Stochastic Number Generator With Bit Permutation Networks.

Vikash Sehwag, N. Prasad, Indrajit Chakrabarti

IEEE Trans. Circuits Syst. II Express Briefs, 2018

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

Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, 2018

2016

TV-PUF : A Fast Lightweight Aging-Resistant Threshold Voltage PUF.

Tanujay Saha, Vikash Sehwag

IACR Cryptol. ePrint Arch., 2016

TV-PUF: A Fast Lightweight Analog Physical Unclonable Function.

Vikash Sehwag, Tanujay Saha

Proceedings of the IEEE International Symposium on Nanoelectronic and Information Systems, 2016

Variation Aware Performance Analysis of TFETs for Low-Voltage Computing.

Vikash Sehwag, Saurav Maji, Mrigank Sharad

Proceedings of the IEEE International Symposium on Nanoelectronic and Information Systems, 2016

2015

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, Mrigank Sharad

CoRR, 2015