Dimensionality Reduction as a Defense against Evasion Attacks on Machine Learning Classifiers. - Arjun Nitin Bhagoji; Daniel Cullina; Prateek Mittal (CoRR, 2017)

Exploring the Space of Black-box Attacks on Deep Neural Networks. - Arjun Nitin Bhagoji; Warren He; Bo Li 026; Dawn Song (CoRR, 2017)

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)

Enhancing robustness of machine learning systems via data transformations. - Arjun Nitin Bhagoji; Daniel Cullina; Chawin Sitawarin; Prateek Mittal (CISS, 2018)

Practical Black-Box Attacks on Deep Neural Networks Using Efficient Query Mechanisms. - Arjun Nitin Bhagoji; Warren He; Bo Li 026; Dawn Song (ECCV (12), 2018)

Black-box Attacks on Deep Neural Networks via Gradient Estimation. - Arjun Nitin Bhagoji; Warren He; Bo Li 026; Dawn Song (ICLR (Workshop), 2018)

PAC-learning in the presence of adversaries. - Daniel Cullina; Arjun Nitin Bhagoji; Prateek Mittal (NeurIPS, 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)

DARTS: Deceiving Autonomous Cars with Toxic Signs. - Chawin Sitawarin; Arjun Nitin Bhagoji; Arsalan Mosenia; Mung Chiang; Prateek Mittal (CoRR, 2018)

PAC-learning in the presence of evasion adversaries. - Daniel Cullina; Arjun Nitin Bhagoji; Prateek Mittal (CoRR, 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)

Analyzing Federated Learning through an Adversarial Lens. - Arjun Nitin Bhagoji; Supriyo Chakraborty; Prateek Mittal; Seraphin B. Calo (ICML, 2019)

Lower Bounds on Adversarial Robustness from Optimal Transport. - Arjun Nitin Bhagoji; Daniel Cullina; Prateek Mittal (NeurIPS, 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)

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; et al. (CoRR, 2019)

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

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

Patch-based Defenses against Web Fingerprinting Attacks. - Shawn Shan; Arjun Nitin Bhagoji; Haitao Zheng 001; Ben Y. Zhao (AISec@CCS, 2021)

Backdoor Attacks Against Deep Learning Systems in the Physical World. - Emily Wenger; Josephine Passananti; Arjun Nitin Bhagoji; Yuanshun Yao; Haitao Zheng 001; Ben Y. Zhao (CVPR, 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)

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)

A Real-time Defense against Website Fingerprinting Attacks. - Shawn Shan; Arjun Nitin Bhagoji; Haitao Zheng 001; Ben Y. Zhao (CoRR, 2021)

Traceback of Data Poisoning Attacks in Neural Networks. - Shawn Shan; Arjun Nitin Bhagoji; Haitao Zheng 001; Ben Y. Zhao (CoRR, 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; et al. (Found. Trends Mach. Learn., 2021)

SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification. - Ashwinee Panda; Saeed Mahloujifar; Arjun Nitin Bhagoji; Supriyo Chakraborty; Prateek Mittal (AISTATS, 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)

Finding Naturally Occurring Physical Backdoors in Image Datasets. - Emily Wenger; Roma Bhattacharjee; Arjun Nitin Bhagoji; Josephine Passananti; Emilio Andere; Heather Zheng; Ben Y. Zhao (NeurIPS, 2022)

Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks. - Shawn Shan; Arjun Nitin Bhagoji; Haitao Zheng 001; Ben Y. Zhao (USENIX Security Symposium, 2022)

Can Backdoor Attacks Survive Time-Varying Models? - Huiying Li 001; Arjun Nitin Bhagoji; Ben Y. Zhao; Haitao Zheng 001 (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)

Natural Backdoor Datasets. - Emily Wenger; Roma Bhattacharjee; Arjun Nitin Bhagoji; Josephine Passananti; Emilio Andere; Haitao Zheng 001; Ben Y. Zhao (CoRR, 2022)

Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning. - Jacob Alexander Markson Brown; Xi Jiang 007; Van Hong Tran; Arjun Nitin Bhagoji; Nguyen Phong Hoang; Nick Feamster; Prateek Mittal; Vinod Yegneswaran (KDD, 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 (NeurIPS, 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 001; Prateek Mittal (CoRR, 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)

"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 018; Jacqueline Mei; Jay L. Shen; Grace Wang; Marshini Chetty; Nick Feamster; Genevieve Lakier; Chenhao Tan (CHI, 2024)

Feasibility of State Space Models for Network Traffic Generation. - Andrew Chu; Xi Jiang 007; Shinan Liu; Arjun Nitin Bhagoji; Francesco Bronzino; Paul Schmitt; Nick Feamster (NAIC, 2024)

Towards Scalable and Robust Model Versioning. - Wenxin Ding; Arjun Nitin Bhagoji; Ben Y. Zhao; Haitao Zheng 001 (SaTML, 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)

NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation. - Xi Jiang 007; Shinan Liu; Aaron Gember-Jacobson; Arjun Nitin Bhagoji; Paul Schmitt; Francesco Bronzino; Nick Feamster (Proc. ACM Meas. Anal. Comput. Syst., 2024)

Silencing Empowerment, Allowing Bigotry: Auditing the Moderation of Hate Speech on Twitch. - Prarabdh Shukla; Wei Yin Chong; Yash Patel; Brennan Schaffner; Danish Pruthi; Arjun Nitin Bhagoji (ACL (1), 2025)

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

NetSSM: Multi-Flow and State-Aware Network Trace Generation using State-Space Models. - Andrew Chu; Xi Jiang 007; Shinan Liu; Arjun Nitin Bhagoji; Francesco Bronzino; Paul Schmitt; Nick Feamster (CoRR, 2025)