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。

身份证件全息图验证的弱监督训练

Weakly Supervised Training for Hologram Verification in Identity  Documents

Face Morphing Attacks pose a threat to the security of identity documents,
especially with respect to a subsequent access control process, because it
enables both individuals involved to exploit the same document. In this study,
face embeddings serve two purposes: pre-selecting images for large-scale
Morphing Attack generation and detecting potential Morphing Attacks. We build
upon previous embedding studies in both use cases using the MagFace model. For
the first objective, we employ an pre-selection algorithm that pairs
individuals based on face embedding similarity. We quantify the attack
potential of differently morphed face images to compare the usability of
pre-selection in automatically generating numerous successful Morphing Attacks.
Regarding the second objective, we compare embeddings from two state-of-the-art
face recognition systems in terms of their ability to detect Morphing Attacks.
Our findings demonstrate that ArcFace and MagFace provide valuable face
embeddings for image pre-selection. Both open-source and COTS face recognition
systems are susceptible to generated attacks, particularly when pre-selection
is based on embeddings rather than random pairing which was only constrained by
soft biometrics. More accurate face recognition systems exhibit greater
vulnerability to attacks, with COTS systems being the most susceptible.
Additionally, MagFace embeddings serve as a robust alternative for detecting
morphed face images compared to the previously used ArcFace embeddings. The
results endorse the advantages of face embeddings in more effective image
pre-selection for face morphing and accurate detection of morphed face images.
This is supported by extensive analysis of various designed attacks. The
MagFace model proves to be a powerful alternative to the commonly used ArcFace
model for both objectives, pre-selection and attack detection.

该研究考察了人脸嵌入在人脸融合攻击中的应用，既可用于生成大量的融合攻击，也可用于检测融合攻击。研究结果表明，MagFace 模型在预选择图像和检测融合攻击方面的效果优于常用的 ArcFace 模型，并且商用人脸识别系统比开源系统更容易受到攻击。

减少变形攻击所需的工作量 —— 深度嵌入用于选择变形配对与改进变形攻击检测

Towards minimizing efforts for Morphing Attacks -- Deep embeddings for  morphing pair selection and improved Morphing Attack Detection

Recognition of identity documents using mobile devices has become a topic of
a wide range of computer vision research. The portfolio of methods and
algorithms for solving such tasks as face detection, document detection and
rectification, text field recognition, and other, is growing, and the scarcity
of datasets has become an important issue. One of the openly accessible
datasets for evaluating such methods is MIDV-500, containing video clips of 50
identity document types in various conditions. However, the variability of
capturing conditions in MIDV-500 did not address some of the key issues, mainly
significant projective distortions and different lighting conditions. In this
paper we present a MIDV-2019 dataset, containing video clips shot with modern
high-resolution mobile cameras, with strong projective distortions and with low
lighting conditions. The description of the added data is presented, and
experimental baselines for text field recognition in different conditions. The
dataset is available for download at
this ftp URL

本文介绍了一个包含在不同条件下使用现代高分辨率移动相机拍摄的影片的 MIDV-2019 数据集，其中包含解决诸如识别身份证件、文本字段识别等问题的方法和算法的方法组合。