  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 metrics but comes at the impractical
cost of acquisition and storage of printed copies. In this work, to overcome
these shortcomings, we design a machine learning (ML) based authentication
system that only requires digital templates and printed original CDP for
training, whereas authentication is based solely on digital templates, which
are used to estimate original printed codes. The obtained results show that the
proposed system can efficiently authenticate original and detect fake CDP by
accurately locating the anomalies in the fake CDP. The empirical evaluation of
the authentication system under investigation is performed on the original and
ML-based fakes CDP printed on two industrial printers.
