  Nowadays, the modern economy critically requires reliable yet cheap
protection solutions against product counterfeiting for the mass market. Copy
detection patterns (CDP) are considered as such solution in several
applications. It is assumed that being printed at the maximum achievable limit
of a printing resolution of an industrial printer with the smallest symbol size
1x1 elements, the CDP cannot be copied with sufficient accuracy and thus are
unclonable. In this paper, we challenge this hypothesis and consider a copy
attack against the CDP based on machine learning. The experimental based on
samples produced on two industrial printers demonstrate that simple detection
metrics used in the CDP authentication cannot reliably distinguish the original
CDP from their fakes. Thus, the paper calls for a need of careful
reconsideration of CDP cloneability and search for new authentication
techniques and CDP optimization because of the current attack.
