114 citations · 417 across the 32 of their papers we have counts for
6 papers · 1 filter
RobustBench: a standardized adversarial robustness benchmark
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag +5
As a research community, we are still lacking a systematic understanding of the progress on adversarial robustness which often makes it hard to identify the most promising ideas in…
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…
Fast-Convergent Federated Learning
Hung T. Nguyen, Vikash Sehwag, Seyyedali Hosseinalipour +3
Federated learning has emerged recently as a promising solution for distributing machine learning tasks through modern networks of mobile devices. Recent studies have obtained lowe…
Time for a Background Check! Uncovering the impact of Background Features on Deep Neural Networks
Vikash Sehwag, Rajvardhan Oak, Mung Chiang +1
With increasing expressive power, deep neural networks have significantly improved the state-of-the-art on image classification datasets, such as ImageNet. In this paper, we invest…
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…
HYDRA: Pruning Adversarially Robust Neural Networks
Vikash Sehwag, Shiqi Wang, Prateek Mittal +1
In safety-critical but computationally resource-constrained applications, deep learning faces two key challenges: lack of robustness against adversarial attacks and large neural ne…