1 code implementation • 15 Feb 2024 • Mariia Drozdova, Vitaliy Kinakh, Omkar Bait, Olga Taran, Erica Lastufka, Miroslava Dessauges-Zavadsky, Taras Holotyak, Daniel Schaerer, Slava Voloshynovskiy
Current techniques, such as CLEAN and PyBDSF, often fail to detect faint sources, highlighting the need for more accurate methods.
no code implementations • 28 Sep 2023 • Yury Belousov, Olga Taran, Vitaliy Kinakh, Slava Voloshynovskiy
Copy detection patterns (CDP) present an efficient technique for product protection against counterfeiting.
no code implementations • 14 Dec 2022 • Joakim Tutt, Olga Taran, Roman Chaban, Brian Pulfer, Yury Belousov, Taras Holotyak, Slava Voloshynovskiy
Nowadays, copy detection patterns (CDP) appear as a very promising anti-counterfeiting technology for physical object protection.
no code implementations • 28 Oct 2022 • Yury Belousov, Brian Pulfer, Roman Chaban, Joakim Tutt, Olga Taran, Taras Holotyak, Slava Voloshynovskiy
In this paper, we address the problem of modeling a printing-imaging channel built on a machine learning approach a. k. a.
no code implementations • 11 Oct 2022 • Roman Chaban, Olga Taran, Joakim Tutt, Yury Belousov, Brian Pulfer, Taras Holotyak, Slava Voloshynovskiy
Since digital off-set printing represents great flexibility in terms of product personalized in comparison with traditional off-set printing, it looks very interesting to address the above concerns for digital off-set printers that are used by several companies for the CDP protection of physical objects.
no code implementations • 29 Sep 2022 • Brian Pulfer, Yury Belousov, Joakim Tutt, Roman Chaban, Olga Taran, Taras Holotyak, Slava Voloshynovskiy
Systems based on classical supervised learning and digital templates assume knowledge of fake CDP at training time and cannot generalize to unseen types of fakes.
no code implementations • 23 Jun 2022 • Brian Pulfer, Roman Chaban, Yury Belousov, Joakim Tutt, Olga Taran, Taras Holotyak, Slava Voloshynovskiy
While Deep Learning (DL) can be used as a part of the authentication system, to the best of our knowledge, none of the previous works has studied the performance of a DL-based authentication system against ML-based attacks on CDP with 1x1 symbol size.
no code implementations • 4 Mar 2022 • Olga Taran, Joakim Tutt, Taras Holotyak, Roman Chaban, Slavi Bonev, Slava Voloshynovskiy
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 of things and brand protection applications.
1 code implementation • 5 Oct 2021 • Roman Chaban, Olga Taran, Joakim Tutt, Taras Holotyak, Slavi Bonev, Slava Voloshynovskiy
Nowadays, the modern economy critically requires reliable yet cheap protection solutions against product counterfeiting for the mass market.
1 code implementation • 31 Aug 2021 • 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-of-the-art performance in classification task.
Ranked #1 on Unsupervised Image Classification on CIFAR-20
no code implementations • 2 Dec 2019 • Slava Voloshynovskiy, Mouad Kondah, Shideh Rezaeifar, Olga Taran, Taras Holotyak, Danilo Jimenez Rezende
In particular, we present a new interpretation of VAE family based on the IB framework using a direct decomposition of mutual information terms and show some interesting connections to existing methods such as VAE [2; 3], beta-VAE [11], AAE [12], InfoVAE [5] and VAE/GAN [13].
no code implementations • 14 May 2019 • Olga Taran, Shideh Rezaeifar, Taras Holotyak, Slava Voloshynovskiy
The robustness of the system is achieved by a specially designed key based randomization.
no code implementations • 8 May 2019 • Shideh Rezaeifar, Behrooz Razeghi, Olga Taran, Taras Holotyak, Slava Voloshynovskiy
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).
1 code implementation • CVPR 2019 • Olga Taran, Shideh Rezaeifar, Taras Holotyak, Slava Voloshynovskiy
The vulnerability of machine learning systems to adversarial attacks questions their usage in many applications.
1 code implementation • 18 Mar 2019 • 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 brand protection applications.
1 code implementation • 10 Sep 2018 • Shideh Rezaeifar, Olga Taran, Slava Voloshynovskiy
We also introduce a criterion based on Kullback-Leibler divergence to reject doubtful examples.
3 code implementations • 5 Sep 2018 • Olga Taran, Shideh Rezaeifar, Slava Voloshynovskiy
The majority of the proposed existing adversarial attacks are based on the differentiability of the DNN cost function. Defence strategies are mostly based on machine learning and signal processing principles that either try to detect-reject or filter out the adversarial perturbations and completely neglect the classical cryptographic component in the defence.
no code implementations • 29 Sep 2017 • Behrooz Razeghi, Slava Voloshynovskiy, Dimche Kostadinov, Olga Taran
The sparsifying transform and privacy amplification are not symmetric for the data owner and data user.