Abstract
The advent of morphing attacks has posed significant security concerns for automated Face Recognition systems, raising the pressing need for robust and effective Morphing Attack Detection (MAD) methods able to effectively address this issue. In this paper, we focus on Differential MAD (D-MAD), where a trusted live capture, usually representing the criminal, is compared with the document image to classify it as morphed or bona fide. We show these approaches based on identity features are effective when the morphed image and the live one are sufficiently diverse; unfortunately, the effectiveness is significantly reduced when the same approaches are applied to look-alike subjects or in all those cases when the similarity between the two compared images is high (e.g. comparison between the morphed image and the accomplice). Therefore, in this paper, we propose ACIdA, a modular D-MAD system, consisting of a module for the attempt type classification, and two modules for the identity and artifacts analysis on input images. Successfully addressing this task would allow broadening the D-MAD applications including, for instance, the document enrollment stage, which currently relies entirely on human evaluation, thus limiting the possibility of releasing ID documents with manipulated images, as well as the automated gates to detect both accomplices and criminals. An extensive cross-dataset experimental evaluation conducted on the introduced scenario shows that ACIdA achieves state-of-the-art results, outperforming literature competitors, while maintaining good performance in traditional D-MAD benchmarks.
Abstract (translated)
随着融合攻击的出现，自动人脸识别系统面临着显著的安全问题，这也使得我们迫切需要能够有效应对这一问题的强大和有效的融合攻击检测（FAD）方法。在本文中，我们重点关注差分FAD（D-FAD），其中可信的活捉通常代表犯罪者，与文档图像进行比较以分类它为融合或真实。我们证明了基于身份特征的方法在融合图像和活捉图像足够 diverse 时是有效的；然而，当同样的方法应用于模拟对象或所有相同图像的相似度很高时（例如，融合图像与同伙的比较），效果会显著降低（例如，融合图像与同伙的比较）。因此，在本文中，我们提出了ACIdA，一种模块化的D-FAD系统，包括一个尝试类型分类模块和一个用于输入图像的身份和 artifacts 分析模块。成功解决此任务将使D-FAD应用范围扩大，包括诸如文档入学阶段等，该阶段完全依赖于人工评估，因此限制了发布带有编辑图像的ID文件以及检测两者（犯罪者和同伙）的可能性。在一个介绍的场景进行的广泛跨数据集实验评估显示，ACIdA取得了最先进的结果，超越了文献竞争对手，同时保持传统D-FAD基准测试中的良好性能。
URL
https://arxiv.org/abs/2404.07667
