6 papers
Dithering Defense: Adversarial Robustness of Vision Foundation Models via Multi-Level Floyd-Steinberg Dithering
Yury Belousov, Brian Pulfer, Vitaliy Kinakh +1
Vision foundation models are widely used as frozen backbones across many downstream tasks, making them a single point of failure under adversarial attack. We study multi-level Floy…
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…
Semi-Supervised Fine-Tuning of Vision Foundation Models with Content-Style Decomposition
Mariia Drozdova, Vitaliy Kinakh, Yury Belousov +2
In this paper, we present a semi-supervised fine-tuning approach designed to improve the performance of pre-trained foundation models on downstream tasks with limited labeled data.…
Evaluation of Security of ML-based Watermarking: Copy and Removal Attacks
Vitaliy Kinakh, Brian Pulfer, Yury Belousov +3
The vast amounts of digital content captured from the real world or AI-generated media necessitate methods for copyright protection, traceability, or data provenance verification.…