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 thanks to the rapid advancement of digital
technologies. To combat these counterfeits, an anti-counterfeiting technology
based on hand-crafted randomness implemented in a form of copy detection
patterns (CDP) is proposed enabling a link between the physical and digital
worlds and being used in various brand protection applications. The modern
mobile phone technologies make the verification process of CDP easier and
available to the end customers. Besides a big interest and attractiveness, the
CDP authentication based on the mobile phone imaging remains insufficiently
studied. In this respect, in this paper we aim at investigating the CDP
authentication under the real-life conditions with the codes printed on an
industrial printer and enrolled via a modern mobile phone under the regular
light conditions. The authentication aspects of the obtained CDP are
investigated with respect to the four types of copy fakes. The impact of fakes’
type used for training of authentication classifier is studied in two
scenarios: (i) supervised binary classification under various assumptions about
the fakes and (ii) one-class classification under unknown fakes. The obtained
results show that the modern machine-learning approaches and the technical
capacity of modern mobile phones allow to make the CDP authentication under
unknown fakes feasible with respect to the considered types of fakes and code

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