  We introduce a new approach to image forensics: placing physical refractive
objects, which we call totems, into a scene so as to protect any photograph
taken of that scene. Totems bend and redirect light rays, thus providing
multiple, albeit distorted, views of the scene within a single image. A
defender can use these distorted totem pixels to detect if an image has been
manipulated. Our approach unscrambles the light rays passing through the totems
by estimating their positions in the scene and using their known geometric and
material properties. To verify a totem-protected image, we detect
inconsistencies between the scene reconstructed from totem viewpoints and the
scene's appearance from the camera viewpoint. Such an approach makes the
adversarial manipulation task more difficult, as the adversary must modify both
the totem and image pixels in a geometrically consistent manner without knowing
the physical properties of the totem. Unlike prior learning-based approaches,
our method does not require training on datasets of specific manipulations, and
instead uses physical properties of the scene and camera to solve the forensics
problem.
