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Protection of physical objects against counterfeiting is an important taskfor the modern economies. In recent years, the high-quality counterfeits appearto be closer to originals thanks to the rapid advancement of digitaltechnologies. To combat these counterfeits, an anti-counterfeiting technologybased on hand-crafted randomness implemented in a form of copy detectionpatterns (CDP) is proposed enabling a link between the physical and digitalworlds and being used in various brand protection applications. The modernmobile phone technologies make the verification process of CDP easier andavailable to the end customers. Besides a big interest and attractiveness, theCDP authentication based on the mobile phone imaging remains insufficientlystudied. In this respect, in this paper we aim at investigating the CDPauthentication under the real-life conditions with the codes printed on anindustrial printer and enrolled via a modern mobile phone under the regularlight conditions. The authentication aspects of the obtained CDP areinvestigated with respect to the four types of copy fakes. The impact of fakes'type used for training of authentication classifier is studied in twoscenarios: (i) supervised binary classification under various assumptions aboutthe fakes and (ii) one-class classification under unknown fakes. The obtainedresults show that the modern machine-learning approaches and the technicalcapacity of modern mobile phones allow to make the CDP authentication underunknown fakes feasible with respect to the considered types of fakes and codedesign.

Mobile authentication of copy detection patterns: how critical is to know fakes?