身份证明文件中全息图验证的弱监督训练
Weakly Supervised Training for Hologram Verification in Identity Documents
AI总结：
本文提出一种基于弱监督训练的身份证明文件全息图远程验证方法，通过优化特征提取与决策流程，在MIDV-HOLO数据集上取得领先性能并有效应对照片替换攻击，为利用无标签数据构建稳健的远程验证系统开辟了新途径。
摘要：
我们提出了一种远程验证身份证明文件中光学可变设备（OVD，通常称为“全息图”）真伪的方法。该方法处理在常见光照条件下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上取得了新的领先性能，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解决照片替换攻击任务的方法，并且可以在真实样本、攻击样本或两者上进行训练以提升性能。通过以极少的监督验证OVD形状和动态特性，这项工作为利用海量无标签数据在商用智能手机上构建稳健的远程身份证明文件验证系统开辟了道路。代码可在 https://github.com/EPITAResearchLab/pouliquen.24.icdar 获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
