  Fingerprint Liveness detection, or presentation attacks detection (PAD), that
is, the ability of detecting if a fingerprint submitted to an electronic
capture device is authentic or made up of some artificial materials, boosted
the attention of the scientific community and recently machine learning
approaches based on deep networks opened novel scenarios. A significant step
ahead was due thanks to the public availability of large sets of data; in
particular, the ones released during the International Fingerprint Liveness
Detection Competition (LivDet). Among others, the fifth edition carried on in
2017, challenged the participants in two more challenges which were not
detailed in the official report. In this paper, we want to extend that report
by focusing on them: the first one was aimed at exploring the case in which the
PAD is integrated into a fingerprint verification systems, where templates of
users are available too and the designer is not constrained to refer only to a
generic users population for the PAD settings. The second one faces with the
exploitation ability of attackers of the provided fakes, and how this ability
impacts on the final performance. These two challenges together may set at
which extent the fingerprint presentation attacks are an actual threat and how
to exploit additional information to make the PAD more effective.
