  Using printed photograph and replaying videos of biometric modalities, such
as iris, fingerprint and face, are common attacks to fool the recognition
systems for granting access as the genuine user. With the growing online
person-to-person shopping (e.g., Ebay and Craigslist), such attacks also
threaten those services, where the online photo illustration might not be
captured from real items but from paper or digital screen. Thus, the study of
anti-spoofing should be extended from modality-specific solutions to
generic-object-based ones. In this work, we define and tackle the problem of
Generic Object Anti-Spoofing (GOAS) for the first time. One significant cue to
detect these attacks is the noise patterns introduced by the capture sensors
and spoof mediums. Different sensor/medium combinations can result in diverse
noise patterns. We propose a GAN-based architecture to synthesize and identify
the noise patterns from seen and unseen medium/sensor combinations. We show
that the procedure of synthesis and identification are mutually beneficial. We
further demonstrate the learned GOAS models can directly contribute to
modality-specific anti-spoofing without domain transfer. The code and GOSet
dataset are available at cvlab.cse.msu.edu/project-goas.html.
