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

翻译：我们提出一种远程验证身份证件中光学可变器件（OVDs，俗称“全息图”）真实性的方法。该方法处理在普通光照条件下使用智能手机拍摄的视频片段，并在两个公开数据集（MIDV-HOLO和MIDV-2020）上进行了评估。得益于弱监督训练，我们优化了特征提取与决策流程，在MIDV-HOLO数据集上取得了领先性能，同时对作为攻击样本的MIDV-2020文档保持了高召回率。这也是迄今为止首个有效应对照片替换攻击任务的方法，且可通过使用真实样本、攻击样本或两者结合训练以提升性能。通过以极少的监督实现OVD形状与动态验证，本研究为利用海量无标注数据在商用智能手机上构建鲁棒的远程身份证件验证系统开辟了道路。代码已开源至 https://github.com/EPITAResearchLab/pouliquen.24.icdar