Arjun Nitin Bhagoji

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 49

Top Topics

Dimensionality Reduction

Pac Learnability

Gradient Estimation

Top Venues

USENIX Security Symposium

Publications

Xi Jiang, Shinan Liu, Aaron Gember-Jacobson, Arjun Nitin Bhagoji, Paul Schmitt, Francesco Bronzino, Nick Feamster
NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation. SIGMETRICS/Performance (Abstracts) (2024)
Wenxin Ding, Arjun Nitin Bhagoji, Ben Y. Zhao, Haitao Zheng
Towards Scalable and Robust Model Versioning. SaTML (2024)
Xi Jiang, Shinan Liu, Aaron Gember-Jacobson, Arjun Nitin Bhagoji, Paul Schmitt, Francesco Bronzino, Nick Feamster
NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation. Proc. ACM Meas. Anal. Comput. Syst. 8 (1) (2024)
Andrew Chu, Xi Jiang, Shinan Liu, Arjun Nitin Bhagoji, Francesco Bronzino, Paul Schmitt, Nick Feamster
Feasibility of State Space Models for Network Traffic Generation. CoRR (2024)
Brennan Schaffner, Arjun Nitin Bhagoji, Siyuan Cheng, Jacqueline Mei, Jay L. Shen, Grace Wang, Marshini Chetty, Nick Feamster, Genevieve Lakier, Chenhao Tan
"Community Guidelines Make this the Best Party on the Internet": An In-Depth Study of Online Platforms' Content Moderation Policies. CHI (2024)
Andrew Chu, Xi Jiang, Shinan Liu, Arjun Nitin Bhagoji, Francesco Bronzino, Paul Schmitt, Nick Feamster
Feasibility of State Space Models for Network Traffic Generation. NAIC (2024)
Brennan Schaffner, Arjun Nitin Bhagoji, Siyuan Cheng, Jacqueline Mei, Jay L. Shen, Grace Wang, Marshini Chetty, Nick Feamster, Genevieve Lakier, Chenhao Tan
"Community Guidelines Make this the Best Party on the Internet": An In-Depth Study of Online Platforms' Content Moderation Policies. CoRR (2024)
Wenxin Ding, Arjun Nitin Bhagoji, Ben Y. Zhao, Haitao Zheng
Towards Scalable and Robust Model Versioning. CoRR (2024)
Xi Jiang, Shinan Liu, Aaron Gember-Jacobson, Arjun Nitin Bhagoji, Paul Schmitt, Francesco Bronzino, Nick Feamster
NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation. CoRR (2023)
Shinan Liu, Francesco Bronzino, Paul Schmitt, Arjun Nitin Bhagoji, Nick Feamster, Hector Garcia Crespo, Timothy Coyle, Brian Ward
LEAF: Navigating Concept Drift in Cellular Networks. PACMNET 1 (CoNEXT2) (2023)
Jacob Alexander Markson Brown, Xi Jiang, Van Tran, Arjun Nitin Bhagoji, Nguyen Phong Hoang, Nick Feamster, Prateek Mittal, Vinod Yegneswaran
Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning. KDD (2023)
Sihui Dai, Wenxin Ding, Arjun Nitin Bhagoji, Daniel Cullina, Ben Y. Zhao, Haitao Zheng, Prateek Mittal
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker. CoRR (2023)
Sihui Dai, Wenxin Ding, Arjun Nitin Bhagoji, Daniel Cullina, Heather Zheng, Ben Zhao, Prateek Mittal
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker. NeurIPS (2023)
Jacob Alexander Markson Brown, Xi Jiang, Van Tran, Arjun Nitin Bhagoji, Nguyen Phong Hoang, Nick Feamster, Prateek Mittal, Vinod Yegneswaran
Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning. CoRR (2023)
Christian Cianfarani, Arjun Nitin Bhagoji, Vikash Sehwag, Ben Y. Zhao, Prateek Mittal
Understanding Robust Learning through the Lens of Representation Similarities. CoRR (2022)
Emily Wenger, Roma Bhattacharjee, Arjun Nitin Bhagoji, Josephine Passananti, Emilio Andere, Heather Zheng, Ben Y. Zhao
Finding Naturally Occurring Physical Backdoors in Image Datasets. NeurIPS (2022)
Huiying Li, Arjun Nitin Bhagoji, Ben Y. Zhao, Haitao Zheng
Can Backdoor Attacks Survive Time-Varying Models? CoRR (2022)
Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, Ben Y. Zhao
Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks. USENIX Security Symposium (2022)
Emily Wenger, Roma Bhattacharjee, Arjun Nitin Bhagoji, Josephine Passananti, Emilio Andere, Haitao Zheng, Ben Y. Zhao
Natural Backdoor Datasets. CoRR (2022)
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)
Ashwinee Panda, Saeed Mahloujifar, Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal
SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification. AISTATS (2022)
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)
Arjun Nitin Bhagoji, Daniel Cullina, Vikash Sehwag, Prateek Mittal
Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries. ICML (2021)
Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, Ben Y. Zhao
Patch-based Defenses against Web Fingerprinting Attacks. AISec@CCS (2021)
Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, Ben Y. Zhao
Traceback of Data Poisoning Attacks in Neural Networks. CoRR (2021)
Ashwinee Panda, Saeed Mahloujifar, Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal
SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification. CoRR (2021)
Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, Ben Y. Zhao
A Real-time Defense against Website Fingerprinting Attacks. CoRR (2021)
Arjun Nitin Bhagoji, Daniel Cullina, Vikash Sehwag, Prateek Mittal
Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries. CoRR (2021)
Emily Wenger, Josephine Passananti, Arjun Nitin Bhagoji, Yuanshun Yao, Haitao Zheng, Ben Y. Zhao
Backdoor Attacks Against Deep Learning Systems in the Physical World. CVPR (2021)
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao
Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 14 (1-2) (2021)
Liwei Song, Vikash Sehwag, Arjun Nitin Bhagoji, Prateek Mittal
A Critical Evaluation of Open-World Machine Learning. CoRR (2020)
Chong Xiang, Arjun Nitin Bhagoji, Vikash Sehwag, Prateek Mittal
PatchGuard: Provable Defense against Adversarial Patches Using Masks on Small Receptive Fields. CoRR (2020)
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, Seraphin B. Calo
Analyzing Federated Learning through an Adversarial Lens. ICML (2019)
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)
Arjun Nitin Bhagoji, Daniel Cullina, Prateek Mittal
Lower Bounds on Adversarial Robustness from Optimal Transport. CoRR (2019)
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 (2019)
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao
Advances and Open Problems in Federated Learning. CoRR (2019)
Arjun Nitin Bhagoji, Daniel Cullina, Prateek Mittal
Lower Bounds on Adversarial Robustness from Optimal Transport. NeurIPS (2019)
Arjun Nitin Bhagoji, Warren He, Bo Li, Dawn Song
Practical Black-Box Attacks on Deep Neural Networks Using Efficient Query Mechanisms. ECCV (12) (2018)
Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Prateek Mittal, Mung Chiang
Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos. CoRR (2018)
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, Seraphin B. Calo
Analyzing Federated Learning through an Adversarial Lens. CoRR (2018)
Daniel Cullina, Arjun Nitin Bhagoji, Prateek Mittal
PAC-learning in the presence of evasion adversaries. CoRR (2018)
Daniel Cullina, Arjun Nitin Bhagoji, Prateek Mittal
PAC-learning in the presence of adversaries. NeurIPS (2018)
Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Mung Chiang, Prateek Mittal
DARTS: Deceiving Autonomous Cars with Toxic Signs. CoRR (2018)
Arjun Nitin Bhagoji, Warren He, Bo Li, Dawn Song
Black-box Attacks on Deep Neural Networks via Gradient Estimation. ICLR (Workshop) (2018)
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)
Arjun Nitin Bhagoji, Daniel Cullina, Chawin Sitawarin, Prateek Mittal
Enhancing robustness of machine learning systems via data transformations. CISS (2018)
Arjun Nitin Bhagoji, Warren He, Bo Li, Dawn Song
Exploring the Space of Black-box Attacks on Deep Neural Networks. CoRR (2017)
Arjun Nitin Bhagoji, Daniel Cullina, Prateek Mittal
Dimensionality Reduction as a Defense against Evasion Attacks on Machine Learning Classifiers. CoRR (2017)