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pro vyhledávání: '"Pulfer, A"'
This paper explores the potential of synthetic physical Copy Detection Patterns (CDP) to improve the robustness of anti-counterfeiting systems. By leveraging synthetic physical CDP, we aim at enhancing security and cost-effectiveness across various r
Externí odkaz:
http://arxiv.org/abs/2410.02575
Autor:
Kinakh, Vitaliy, Pulfer, Brian, Belousov, Yury, Fernandez, Pierre, Furon, Teddy, Voloshynovskiy, Slava
The vast amounts of digital content captured from the real world or AI-generated media necessitate methods for copyright protection, traceability, or data provenance verification. Digital watermarking serves as a crucial approach to address these cha
Externí odkaz:
http://arxiv.org/abs/2409.18211
Autor:
Tutt, Joakim, Taran, Olga, Chaban, Roman, Pulfer, Brian, Belousov, Yury, Holotyak, Taras, Voloshynovskiy, Slava
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 attacking tool has shown that the general authentication schemes are
Externí odkaz:
http://arxiv.org/abs/2212.07326
Accurately forecasting the weather is an important task, as many real-world processes and decisions depend on future meteorological conditions. The NeurIPS 2022 challenge entitled Weather4cast poses the problem of predicting rainfall events for the n
Externí odkaz:
http://arxiv.org/abs/2212.02456
Autor:
Belousov, Yury, Pulfer, Brian, Chaban, Roman, Tutt, Joakim, Taran, Olga, Holotyak, Taras, Voloshynovskiy, Slava
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 copy detection patterns (CDP). The digital twin is formulated on an in
Externí odkaz:
http://arxiv.org/abs/2210.17420
Autor:
Chaban, Roman, Taran, Olga, Tutt, Joakim, Belousov, Yury, Pulfer, Brian, Holotyak, Taras, Voloshynovskiy, Slava
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 industry due to its numerous benefits in comparison to alternative prot
Externí odkaz:
http://arxiv.org/abs/2210.05343
Autor:
Pulfer, Brian, Belousov, Yury, Tutt, Joakim, Chaban, Roman, Taran, Olga, Holotyak, Taras, Voloshynovskiy, Slava
Copy detection patterns (CDP) are recent technologies for protecting products from counterfeiting. However, in contrast to traditional copy fakes, deep learning-based fakes have shown to be hardly distinguishable from originals by traditional authent
Externí odkaz:
http://arxiv.org/abs/2209.15625
Autor:
Pulfer, Brian, Chaban, Roman, Belousov, Yury, Tutt, Joakim, Taran, Olga, Holotyak, Taras, Voloshynovskiy, Slava
Copy detection patterns (CDP) are an attractive technology that allows manufacturers to defend their products against counterfeiting. The main assumption behind the protection mechanism of CDP is that these codes printed with the smallest symbol size
Externí odkaz:
http://arxiv.org/abs/2206.11793
Autor:
Kofel, Donato, Bourgeois, Ilann, Paganini, Romana, Pulfer, Aurèle, Grossiord, Charlotte, Schmale, Julia
Publikováno v:
In Urban Forestry & Urban Greening November 2024 101
Safe deployment of self-driving cars (SDC) necessitates thorough simulated and in-field testing. Most testing techniques consider virtualized SDCs within a simulation environment, whereas less effort has been directed towards assessing whether such t
Externí odkaz:
http://arxiv.org/abs/2112.11255