23 citations · 36 across the 13 of their papers we have counts for
16 papers
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…
Privacy-Preserving Image Sharing via Sparsifying Layers on Convolutional Groups
Sohrab Ferdowsi, Behrooz Razeghi, Taras Holotyak +2
We propose a practical framework to address the problem of privacy-aware image sharing in large-scale setups. We argue that, while compactness is always desired at scale, this need…
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…
Clustering with Jointly Learned Nonlinear Transforms Over Discriminating Min-Max Similarity/Dissimilarity Assignment
Dimche Kostadinov, Behrooz Razeghi, Taras Holotyak +1
This paper presents a novel clustering concept that is based on jointly learned nonlinear transforms (NTs) with priors on the information loss and the discrimination. We introduce…
Sparse Ternary Codes for similarity search have higher coding gain than dense binary codes
Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov +1
This paper addresses the problem of Approximate Nearest Neighbor (ANN) search in pattern recognition where feature vectors in a database are encoded as compact codes in order to sp…