Arjun Nitin Bhagoji

According to our database, Arjun Nitin Bhagoji authored at least 40 papers between 2017 and 2024.

Collaborative distances:

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

2024

NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation.

Xi Jiang, Shinan Liu, Aaron Gember-Jacobson, Arjun Nitin Bhagoji, Paul Schmitt, Francesco Bronzino, Nick Feamster

Proc. ACM Meas. Anal. Comput. Syst., 2024

MYCROFT: Towards Effective and Efficient External Data Augmentation.

Zain Sarwar, Van Tran, Arjun Nitin Bhagoji, Nick Feamster, Ben Y. Zhao, Supriyo Chakraborty

CoRR, 2024

Towards Scalable and Robust Model Versioning.

Wenxin Ding, Arjun Nitin Bhagoji, Ben Y. Zhao, Haitao Zheng

Proceedings of the IEEE Conference on Secure and Trustworthy Machine Learning, 2024

Feasibility of State Space Models for Network Traffic Generation.

Andrew Chu, Xi Jiang, Shinan Liu, Arjun Nitin Bhagoji, Francesco Bronzino, Paul Schmitt, Nick Feamster

Proceedings of the 2024 SIGCOMM Workshop on Networks for AI Computing, 2024

"Community Guidelines Make this the Best Party on the Internet": An In-Depth Study of Online Platforms' Content Moderation Policies.

Brennan Schaffner, Arjun Nitin Bhagoji, Siyuan Cheng, Jacqueline Mei, Jay L. Shen, Grace Wang, Marshini Chetty, Nick Feamster, Genevieve Lakier, Chenhao Tan

Proceedings of the CHI Conference on Human Factors in Computing Systems, 2024

2023

LEAF: Navigating Concept Drift in Cellular Networks.

Shinan Liu, Francesco Bronzino, Paul Schmitt, Arjun Nitin Bhagoji, Nick Feamster, Hector Garcia Crespo, Timothy Coyle, Brian Ward

PACMNET, 2023

Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker.

Sihui Dai, Wenxin Ding, Arjun Nitin Bhagoji, Daniel Cullina, Ben Y. Zhao, Haitao Zheng, Prateek Mittal

CoRR, 2023

Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker.

Sihui Dai, Wenxin Ding, Arjun Nitin Bhagoji, Daniel Cullina, Heather Zheng, Ben Zhao, Prateek Mittal

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

Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning.

Jacob Alexander Markson Brown, Xi Jiang, Van Hong Tran, Arjun Nitin Bhagoji, Nguyen Phong Hoang, Nick Feamster, Prateek Mittal, Vinod Yegneswaran

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

2022

Natural Backdoor Datasets.

Emily Wenger, Roma Bhattacharjee, Arjun Nitin Bhagoji, Josephine Passananti, Emilio Andere, Haitao Zheng, Ben Y. Zhao

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

Can Backdoor Attacks Survive Time-Varying Models?

Huiying Li, Arjun Nitin Bhagoji, Ben Y. Zhao, Haitao Zheng

CoRR, 2022

Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks.

Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, Ben Y. Zhao

Proceedings of the 31st USENIX Security Symposium, 2022

Finding Naturally Occurring Physical Backdoors in Image Datasets.

Emily Wenger, Roma Bhattacharjee, Arjun Nitin Bhagoji, Josephine Passananti, Emilio Andere, Heather Zheng, Ben Y. Zhao

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

SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification.

Ashwinee Panda, Saeed Mahloujifar, Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Advances and Open Problems in Federated Learning.

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

Found. Trends Mach. Learn., 2021

Traceback of Data Poisoning Attacks in Neural Networks.

Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, Ben Y. Zhao

CoRR, 2021

A Real-time Defense against Website Fingerprinting Attacks.

Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, Ben Y. Zhao

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

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

Backdoor Attacks Against Deep Learning Systems in the Physical World.

Emily Wenger, Josephine Passananti, Arjun Nitin Bhagoji, Yuanshun Yao, Haitao Zheng, Ben Y. Zhao

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Patch-based Defenses against Web Fingerprinting Attacks.

Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, Ben Y. Zhao

Proceedings of the AISec@CCS 2021: Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security, 2021

2020

A Critical Evaluation of Open-World Machine Learning.

Liwei Song, Vikash Sehwag, Arjun Nitin Bhagoji, 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

2019

Advances and Open Problems in Federated Learning.

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

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

Lower Bounds on Adversarial Robustness from Optimal Transport.

Arjun Nitin Bhagoji, Daniel Cullina, Prateek Mittal

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Analyzing Federated Learning through an Adversarial Lens.

Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, Seraphin B. Calo

Proceedings of the 36th International Conference on Machine Learning, 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

PAC-learning in the presence of evasion adversaries.

Daniel Cullina, Arjun Nitin Bhagoji, Prateek Mittal

CoRR, 2018

DARTS: Deceiving Autonomous Cars with Toxic Signs.

Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Mung Chiang, Prateek Mittal

CoRR, 2018

Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos.

Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Prateek Mittal, Mung Chiang

CoRR, 2018

PAC-learning in the presence of adversaries.

Daniel Cullina, Arjun Nitin Bhagoji, Prateek Mittal

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Black-box Attacks on Deep Neural Networks via Gradient Estimation.

Arjun Nitin Bhagoji, Warren He, Bo Li, Dawn Song

Proceedings of the 6th International Conference on Learning Representations, 2018

Practical Black-Box Attacks on Deep Neural Networks Using Efficient Query Mechanisms.

Arjun Nitin Bhagoji, Warren He, Bo Li, Dawn Song

Proceedings of the Computer Vision - ECCV 2018, 2018

Enhancing robustness of machine learning systems via data transformations.

Arjun Nitin Bhagoji, Daniel Cullina, Chawin Sitawarin, Prateek Mittal

Proceedings of the 52nd Annual Conference on Information Sciences and Systems, 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

2017

Exploring the Space of Black-box Attacks on Deep Neural Networks.

Arjun Nitin Bhagoji, Warren He, Bo Li, Dawn Song

CoRR, 2017

Dimensionality Reduction as a Defense against Evasion Attacks on Machine Learning Classifiers.

Arjun Nitin Bhagoji, Daniel Cullina, Prateek Mittal

CoRR, 2017