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Anomaly localization for copy detection patterns through print estimations. (arXiv:2209.15625v1 [cs.CV])
Oct. 3, 2022, 1:15 a.m. | Brian Pulfer, Yury Belousov, Joakim Tutt, Roman Chaban, Olga Taran, Taras Holotyak, Slava Voloshynovskiy
cs.CV updates on arXiv.org arxiv.org
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 authentication systems. 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. Authentication based on printed
copies of originals is an alternative that yields better results even for
unseen fakes and simple authentication …
More from arxiv.org / cs.CV updates on arXiv.org
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