  Recent advances in media generation techniques have made it easier for
attackers to create forged images and videos. State-of-the-art methods enable
the real-time creation of a forged version of a single video obtained from a
social network. Although numerous methods have been developed for detecting
forged images and videos, they are generally targeted at certain domains and
quickly become obsolete as new kinds of attacks appear. The method introduced
in this paper uses a capsule network to detect various kinds of spoofs, from
replay attacks using printed images or recorded videos to computer-generated
videos using deep convolutional neural networks. It extends the application of
capsule networks beyond their original intention to the solving of inverse
graphics problems.
