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Traceback of Data Poisoning Attacks in Neural Networks


Oct 13, 2021
Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, Ben Y. Zhao

* 18 pages 

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


Apr 16, 2021
Arjun Nitin Bhagoji, Daniel Cullina, Vikash Sehwag, Prateek Mittal

* 16 pages, 12 figures; Under review 

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A Critical Evaluation of Open-World Machine Learning


Jul 08, 2020
Liwei Song, Vikash Sehwag, Arjun Nitin Bhagoji, Prateek Mittal

* Presented at the ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning 

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


Jun 08, 2020
Chong Xiang, Arjun Nitin Bhagoji, Vikash Sehwag, Prateek Mittal


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Advances and Open Problems in Federated Learning


Dec 10, 2019
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith 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, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, 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


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Lower Bounds on Adversarial Robustness from Optimal Transport


Oct 30, 2019
Arjun Nitin Bhagoji, Daniel Cullina, Prateek Mittal

* Accepted for the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019); 18 pages, 5 figures 

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Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples


May 05, 2019
Vikash Sehwag, Arjun Nitin Bhagoji, Liwei Song, Chawin Sitawarin, Daniel Cullina, Mung Chiang, Prateek Mittal

* 18 pages, 5 figures, 9 tables 

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Analyzing Federated Learning through an Adversarial Lens


Nov 29, 2018
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, Seraphin Calo

* 18 pages, 12 figures 

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PAC-learning in the presence of evasion adversaries


Jun 06, 2018
Daniel Cullina, Arjun Nitin Bhagoji, Prateek Mittal

* 14 pages, 2 figures (minor changes to biblatex output) 

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DARTS: Deceiving Autonomous Cars with Toxic Signs


May 31, 2018
Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Mung Chiang, Prateek Mittal

* Submitted to ACM CCS 2018; Extended version of [1801.02780] Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos 

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


Mar 26, 2018
Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Prateek Mittal, Mung Chiang

* Extended abstract accepted for the 1st Deep Learning and Security Workshop; 5 pages, 4 figures 

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


Dec 27, 2017
Arjun Nitin Bhagoji, Warren He, Bo Li, Dawn Song

* 25 pages, 7 figures, 10 tables 

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Enhancing Robustness of Machine Learning Systems via Data Transformations


Nov 29, 2017
Arjun Nitin Bhagoji, Daniel Cullina, Chawin Sitawarin, Prateek Mittal

* 15 pages 

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