  All existing databases of spoofed speech contain attack data that is spoofed
in its entirety. In practice, it is entirely plausible that successful attacks
can be mounted with utterances that are only partially spoofed. By definition,
partially-spoofed utterances contain a mix of both spoofed and bona fide
segments, which will likely degrade the performance of countermeasures trained
with entirely spoofed utterances. This hypothesis raises the obvious question:
'Can we detect partially-spoofed audio?' This paper introduces a new database
of partially-spoofed data, named PartialSpoof, to help address this question.
This new database enables us to investigate and compare the performance of
countermeasures on both utterance- and segmental- level labels. Experimental
results using the utterance-level labels reveal that the reliability of
countermeasures trained to detect fully-spoofed data is found to degrade
substantially when tested with partially-spoofed data, whereas training on
partially-spoofed data performs reliably in the case of both fully- and
partially-spoofed utterances. Additional experiments using segmental-level
labels show that spotting injected spoofed segments included in an utterance is
a much more challenging task even if the latest countermeasure models are used.
