  In online markets, sellers can maliciously recapture others' images on
display screens to utilize as spoof images, which can be challenging to
distinguish in human eyes. To prevent such harm, we propose an anti-spoofing
method using the paired rgb images and depth maps provided by the mobile camera
with a Time-of-Fight sensor. When images are recaptured on display screens,
various patterns differing by the screens as known as the moir\'e patterns can
be also captured in spoof images. These patterns lead the anti-spoofing model
to be overfitted and unable to detect spoof images recaptured on unseen media.
To avoid the issue, we build a novel representation model composed of two
embedding models, which can be trained without considering the recaptured
images. Also, we newly introduce mToF dataset, the largest and most diverse
object anti-spoofing dataset, and the first to utilize ToF data. Experimental
results confirm that our model achieves robust generalization even across
unseen domains.
