  We propose a method to remotely verify the authenticity of Optically Variable
Devices (OVDs), often referred to as ``holograms'', in identity documents. Our
method processes video clips captured with smartphones under common lighting
conditions, and is evaluated on two public datasets: MIDV-HOLO and MIDV-2020.
Thanks to a weakly-supervised training, we optimize a feature extraction and
decision pipeline which achieves a new leading performance on MIDV-HOLO, while
maintaining a high recall on documents from MIDV-2020 used as attack samples.
It is also the first method, to date, to effectively address the photo
replacement attack task, and can be trained on either genuine samples, attack
samples, or both for increased performance. By enabling to verify OVD shapes
and dynamics with very little supervision, this work opens the way towards the
use of massive amounts of unlabeled data to build robust remote identity
document verification systems on commodity smartphones. Code is available at
https://github.com/EPITAResearchLab/pouliquen.24.icdar
