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- research-articleFebruary 2024
A Machine Learning-Based Digital Twin for Anti-Counterfeiting Applications With Copy Detection Patterns
- Yury Belousov,
- Guillaume Quétant,
- Brian Pulfer,
- Roman Chaban,
- Joakim Tutt,
- Olga Taran,
- Taras Holotyak,
- Slava Voloshynovskiy
IEEE Transactions on Information Forensics and Security (TIFS), Volume 192024, Pages 3395–3408https://doi.org/10.1109/TIFS.2024.3361798In this paper, we present a new approach to model a printing-imaging channel using a machine learning-based “digital twin” for copy detection patterns (CDP). The CDP are considered as modern anti-counterfeiting features in multiple ...
- research-articleJanuary 2024
Authentication of Copy Detection Patterns: A Pattern Reliability Based Approach
IEEE Transactions on Information Forensics and Security (TIFS), Volume 192024, Pages 3124–3134https://doi.org/10.1109/TIFS.2024.3359510Copy Detection Pattern (CDP) technology is a promising anti-counterfeiting solution for the protection of physical goods. In recent years, it has been shown that this technology is threatened by powerful deep learning attacks that are able to bypass ...
- ArticleJanuary 2019
Bridging Machine Learning and Cryptography in Defence Against Adversarial Attacks
Computer Vision – ECCV 2018 WorkshopsSep 2018, Pages 267–279https://doi.org/10.1007/978-3-030-11012-3_23AbstractIn 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 deep learning architectures are vulnerable to so called adversarial ...