activity
20152024
most citedExploring the Space of Black-box Attacks on Deep Neural Networks

70 citations · 156 across the 7 of their papers we have counts for

collaborators

14 papers

cs.HC202422 cited

"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 +7

Moderating user-generated content on online platforms is crucial for balancing user safety and freedom of speech. Particularly in the United States, platforms are not subject to le…

cs.LG2021

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

Arjun Nitin Bhagoji, Daniel Cullina, Vikash Sehwag +1

Understanding the fundamental limits of robust supervised learning has emerged as a problem of immense interest, from both practical and theoretical standpoints. In particular, it…

cs.CR202116 cited

A Real-time Defense against Website Fingerprinting Attacks

Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng +1

Anonymity systems like Tor are vulnerable to Website Fingerprinting (WF) attacks, where a local passive eavesdropper infers the victim's activity. Current WF attacks based on deep…

cs.LG20208 cited

A Critical Evaluation of Open-World Machine Learning

Liwei Song, Vikash Sehwag, Arjun Nitin Bhagoji +1

Open-world machine learning (ML) combines closed-world models trained on in-distribution data with out-of-distribution (OOD) detectors, which aim to detect and reject OOD inputs. P…

cs.CV2020

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

Chong Xiang, Arjun Nitin Bhagoji, Vikash Sehwag +1

Localized adversarial patches aim to induce misclassification in machine learning models by arbitrarily modifying pixels within a restricted region of an image. Such attacks can be…

cs.LG2019

Advances and Open Problems in Federated Learning

Peter Kairouz, H. Brendan McMahan, Brendan Avent +56

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…