  Presentation attacks are recurrent threats to biometric systems, where
impostors attempt to bypass these systems. Humans often use background
information as contextual cues for their visual system. Yet, regarding
face-based systems, the background is often discarded, since face presentation
attack detection (PAD) models are mostly trained with face crops. This work
presents a comparative study of face PAD models (including multi-task learning,
adversarial training and dynamic frame selection) in two settings: with and
without crops. The results show that the performance is consistently better
when the background is present in the images. The proposed multi-task
methodology beats the state-of-the-art results on the ROSE-Youtu dataset by a
large margin with an equal error rate of 0.2%. Furthermore, we analyze the
models' predictions with Grad-CAM++ with the aim to investigate to what extent
the models focus on background elements that are known to be useful for human
inspection. From this analysis we can conclude that the background cues are not
relevant across all the attacks. Thus, showing the capability of the model to
leverage the background information only when necessary.
