23 citations · 81 across the 41 of their papers we have counts for
48 papers
NAWE: Digital Watermarking with Neural-Assisted Watermark Extraction
Roman Chaban, Vitaliy Kinakh, Lilian Rouzaire +1
NAWE (Neural-Assisted Watermark Extraction) combines an explicit signal-processing watermarking construction with a pretrained neural host predictor. A periodic, perceptually maske…
Authentication of Copy Detection Patterns via Cross-Camera Dual-Synthetic Referencing
Ivan Oleksiyuk, Roman Chaban, Slava Voloshynovskiy
Copy Detection Patterns (CDPs) are structures printed on physical objects to enable cost-effective authentication. Verification is achieved by comparing a captured image with the d…
Human-Inspired Context-Selective Multimodal Memory for Social Robots
Hangyeol Kang, Slava Voloshynovskiy, Nadia Magnenat Thalmann
Memory is fundamental to social interaction, enabling humans to recall meaningful past experiences and adapt their behavior accordingly based on the context. However, most current…
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,…
TwinTURBO: Semi-Supervised Fine-Tuning of Foundation Models via Mutual Information Decompositions for Downstream Task and Latent Spaces
Guillaume Quétant, Pavlo Molchanov, Slava Voloshynovskiy
We present a semi-supervised fine-tuning framework for foundation models that utilises mutual information decomposition to address the challenges of training for a limited amount o…
Provable Performance Guarantees of Copy Detection Patterns
Joakim Tutt, Slava Voloshynovskiy
Copy Detection Patterns (CDPs) are crucial elements in modern security applications, playing a vital role in safeguarding industries such as food, pharmaceuticals, and cosmetics. C…
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…
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…
The Mass-ive Issue: Anomaly Detection in Jet Physics
Tobias Golling, Takuya Nobe, Dimitrios Proios +8
In the hunt for new and unobserved phenomena in particle physics, attention has turned in recent years to using advanced machine learning techniques for model independent searches.…
MV-MR: multi-views and multi-representations for self-supervised learning and knowledge distillation
Vitaliy Kinakh, Mariia Drozdova, Slava Voloshynovskiy
We present a new method of self-supervised learning and knowledge distillation based on the multi-views and multi-representations (MV-MR). The MV-MR is based on the maximization of…
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…
Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utility
Behrooz Razeghi, Flavio P. Calmon, Deniz Gunduz +1
Bottleneck problems are an important class of optimization problems that have recently gained increasing attention in the domain of machine learning and information theory. They ar…
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…
Turbo-Sim: a generalised generative model with a physical latent space
Guillaume Quétant, Mariia Drozdova, Vitaliy Kinakh +2
We present Turbo-Sim, a generalised autoencoder framework derived from principles of information theory that can be used as a generative model. By maximising the mutual information…
Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN
Vitaliy Kinakh, Mariia Drozdova, Guillaume Quétant +2
Conditional generation is a subclass of generative problems where the output of the generation is conditioned by the attribute information. In this paper, we present a stochastic c…
Generation of data on discontinuous manifolds via continuous stochastic non-invertible networks
Mariia Drozdova, Vitaliy Kinakh, Guillaume Quétant +2
The generation of discontinuous distributions is a difficult task for most known frameworks such as generative autoencoders and generative adversarial networks. Generative non-inve…
Funnels: Exact maximum likelihood with dimensionality reduction
Samuel Klein, John A. Raine, Sebastian Pina-Otey +2
Normalizing flows are diffeomorphic, typically dimension-preserving, models trained using the likelihood of the model. We use the SurVAE framework to construct dimension reducing s…
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…