4 citations · 7 across the 11 of their papers we have counts for
3 papers · 1 filter
Beyond Classification: Evaluating Diffusion Denoised Smoothing for Security-Utility Trade off
Yury Belousov, Brian Pulfer, Vitaliy Kinakh +1
While foundation models demonstrate impressive performance across various tasks, they remain vulnerable to adversarial inputs. Current research explores various approaches to enhan…
Robustness Tokens: Towards Adversarial Robustness of Transformers
Brian Pulfer, Yury Belousov, Slava Voloshynovskiy
Recently, large pre-trained foundation models have become widely adopted by machine learning practitioners for a multitude of tasks. Given that such models are publicly available,…
Task-Agnostic Attacks Against Vision Foundation Models
Brian Pulfer, Yury Belousov, Vitaliy Kinakh +2
The study of security in machine learning mainly focuses on downstream task-specific attacks, where the adversarial example is obtained by optimizing a loss function specific to th…