6 citations · 23 across the 15 of their papers we have counts for
16 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.…
TURBO: The Swiss Knife of Auto-Encoders
Guillaume Quétant, Yury Belousov, Vitaliy Kinakh +1
We present a novel information-theoretic framework, termed as TURBO, designed to systematically analyse and generalise auto-encoding methods. We start by examining the principles o…
Stochastic Digital Twin for Copy Detection Patterns
Yury Belousov, Olga Taran, Vitaliy Kinakh +1
Copy detection patterns (CDP) present an efficient technique for product protection against counterfeiting. However, the complexity of studying CDP production variability often res…
Mathematical model of printing-imaging channel for blind detection of fake copy detection patterns
Joakim Tutt, Olga Taran, Roman Chaban +4
Nowadays, copy detection patterns (CDP) appear as a very promising anti-counterfeiting technology for physical object protection. However, the advent of deep learning as a powerful…
Solving the Weather4cast Challenge via Visual Transformers for 3D Images
Yury Belousov, Sergey Polezhaev, Brian Pulfer
Accurately forecasting the weather is an important task, as many real-world processes and decisions depend on future meteorological conditions. The NeurIPS 2022 challenge entitled…
Digital twins of physical printing-imaging channel
Yury Belousov, Brian Pulfer, Roman Chaban +4
In this paper, we address the problem of modeling a printing-imaging channel built on a machine learning approach a.k.a. digital twin for anti-counterfeiting applications based on…
Printing variability of copy detection patterns
Roman Chaban, Olga Taran, Joakim Tutt +4
Copy detection pattern (CDP) is a novel solution for products' protection against counterfeiting, which gains its popularity in recent years. CDP attracts the anti-counterfeiting i…
Anomaly localization for copy detection patterns through print estimations
Brian Pulfer, Yury Belousov, Joakim Tutt +4
Copy detection patterns (CDP) are recent technologies for protecting products from counterfeiting. However, in contrast to traditional copy fakes, deep learning-based fakes have sh…
Authentication of Copy Detection Patterns under Machine Learning Attacks: A Supervised Approach
Brian Pulfer, Roman Chaban, Yury Belousov +4
Copy detection patterns (CDP) are an attractive technology that allows manufacturers to defend their products against counterfeiting. The main assumption behind the protection mech…
MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned
Anssi Kanervisto, Stephanie Milani, Karolis Ramanauskas +19
Reinforcement learning competitions advance the field by providing appropriate scope and support to develop solutions toward a specific problem. To promote the development of more…
Solving Black-Box Optimization Challenge via Learning Search Space Partition for Local Bayesian Optimization
Mikita Sazanovich, Anastasiya Nikolskaya, Yury Belousov +1
Black-box optimization is one of the vital tasks in machine learning, since it approximates real-world conditions, in that we do not always know all the properties of a given syste…