23 citations · 49 across the 19 of their papers we have counts for
22 papers
Benchmarking 2D Egocentric Hand Pose Datasets
Olga Taran, Damian M. Manzone, Jose Zariffa
Hand pose estimation from egocentric video has broad implications across various domains, including human-computer interaction, assistive technologies, activity recognition, and ro…
Self-Supervised Learning on MeerKAT Wide-Field Continuum Images
Erica Lastufka, Omkar Bait, Olga Taran +8
Self-supervised learning (SSL) applied to natural images has demonstrated a remarkable ability to learn meaningful, low-dimension representations without labels, resulting in model…
Radio-astronomical Image Reconstruction with Conditional Denoising Diffusion Model
Mariia Drozdova, Vitaliy Kinakh, Omkar Bait +6
Reconstructing sky models from dirty radio images for accurate source localization and flux estimation is crucial for studying galaxy evolution at high redshift, especially in deep…
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…
Challenging interferometric imaging: Machine learning-based source localization from uv-plane observations
O. Taran, O. Bait, M. Dessauges-Zavadsky +3
In our work, we examine, for the first time, the possibility of fast and efficient source localization directly from the uvobservations, omitting the recovering of the dirty or cle…
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…
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…
Mobile authentication of copy detection patterns
Olga Taran, Joakim Tutt, Taras Holotyak +3
In the recent years, the copy detection patterns (CDP) attracted a lot of attention as a link between the physical and digital worlds, which is of great interest for the internet o…
Machine learning attack on copy detection patterns: are 1x1 patterns cloneable?
Roman Chaban, Olga Taran, Joakim Tutt +3
Nowadays, the modern economy critically requires reliable yet cheap protection solutions against product counterfeiting for the mass market. Copy detection patterns (CDP) are consi…
Mobile authentication of copy detection patterns: how critical is to know fakes?
Olga Taran, Joakim Tutt, Taras Holotyak +3
Protection of physical objects against counterfeiting is an important task for the modern economies. In recent years, the high-quality counterfeits appear to be closer to originals…
ScatSimCLR: self-supervised contrastive learning with pretext task regularization for small-scale datasets
Vitaliy Kinakh, Olga Taran, Svyatoslav Voloshynovskiy
In this paper, we consider a problem of self-supervised learning for small-scale datasets based on contrastive loss between multiple views of the data, which demonstrates the state…
Information bottleneck through variational glasses
Slava Voloshynovskiy, Mouad Kondah, Shideh Rezaeifar +3
Information bottleneck (IB) principle [1] has become an important element in information-theoretic analysis of deep models. Many state-of-the-art generative models of both Variatio…
Robustification of deep net classifiers by key based diversified aggregation with pre-filtering
Olga Taran, Shideh Rezaeifar, Taras Holotyak +1
In this paper, we address a problem of machine learning system vulnerability to adversarial attacks. We propose and investigate a Key based Diversified Aggregation (KDA) mechanism…
Reconstruction of Privacy-Sensitive Data from Protected Templates
Shideh Rezaeifar, Behrooz Razeghi, Olga Taran +2
In this paper, we address the problem of data reconstruction from privacy-protected templates, based on recent concept of sparse ternary coding with ambiguization (STCA). The STCA…
Defending against adversarial attacks by randomized diversification
Olga Taran, Shideh Rezaeifar, Taras Holotyak +1
The vulnerability of machine learning systems to adversarial attacks questions their usage in many applications. In this paper, we propose a randomized diversification as a defense…
Clonability of anti-counterfeiting printable graphical codes: a machine learning approach
Olga Taran, Slavi Bonev, Slava Voloshynovskiy
In recent years, printable graphical codes have attracted a lot of attention enabling a link between the physical and digital worlds, which is of great interest for the IoT and bra…
Classification by Re-generation: Towards Classification Based on Variational Inference
Shideh Rezaeifar, Olga Taran, Slava Voloshynovskiy
As Deep Neural Networks (DNNs) are considered the state-of-the-art in many classification tasks, the question of their semantic generalizations has been raised. To address semantic…
Bridging machine learning and cryptography in defence against adversarial attacks
Olga Taran, Shideh Rezaeifar, Slava Voloshynovskiy
In the last decade, deep learning algorithms have become very popular thanks to the achieved performance in many machine learning and computer vision tasks. However, most of the de…
Privacy Preserving Identification Using Sparse Approximation with Ambiguization
Behrooz Razeghi, Slava Voloshynovskiy, Dimche Kostadinov +1
In this paper, we consider a privacy preserving encoding framework for identification applications covering biometrics, physical object security and the Internet of Things (IoT). T…