Morphed face images have recently become a growing concern for existing face
verification systems, as they are relatively easy to generate and can be used
to impersonate someone's identity for various malicious purposes. Efficient
Morphing Attack Detection (MAD) that generalizes well across different morphing
techniques is, therefore, of paramount importance. Existing MAD techniques
predominantly rely on discriminative models that learn from examples of bona
fide and morphed images and, as a result, often exhibit sub-optimal
generalization performance when confronted with unknown types of morphing
attacks. To address this problem, we propose a novel, diffusion-based MAD
method in this paper that learns only from the characteristics of bona fide
images. Various forms of morphing attacks are then detected by our model as
out-of-distribution samples. We perform rigorous experiments over four
different datasets (CASIA-WebFace, FRLL-Morphs, FERET-Morphs and FRGC-Morphs)
and compare the proposed solution to both discriminatively-trained and
once-class MAD models. The experimental results show that our MAD model
achieves highly competitive results on all considered datasets.

本文介绍了一种基于扩散的新型高效 MAD 方法，学习纯粹的真实图像的特征，实现对不同形态的变形攻击的检测。在四个不同数据集上进行了严格的实验，并与现有解决方案进行了比较，结果表明我们的 MAD 模型在所有数据集上都取得了高竞争力的结果。