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
this https URL

提出一种方法，通过在普通光照条件下处理智能手机捕捉的视频片段，远程验证身份文件中的光变设备（常称为 “全息图”），并在两个公共数据集上进行评估。该方法采用弱监督训练，优化特征提取和决策流程，实现了对 MIDV-HOLO 的新领先性能，并在作为攻击样本使用的 MIDV-2020 文档上保持了高召回率。这也是首个有效解决照片替换攻击任务的方法，可以根据需要训练真实样本、攻击样本或二者兼而有之，以提高性能。通过能够在很少的监督下验证 OVD 的形状和动态，这项工作为在普通智能手机上构建具有鲁棒性的远程身份文件验证系统提供了新途径。