Abstract
Face morphing attacks have posed severe threats to Face Recognition Systems (FRS), which are operated in border control and passport issuance use cases. Correspondingly, morphing attack detection algorithms (MAD) are needed to defend against such attacks. MAD approaches must be robust enough to handle unknown attacks in an open-set scenario where attacks can originate from various morphing generation algorithms, post-processing and the diversity of printers/scanners. The problem of generalization is further pronounced when the detection has to be made on a single suspected image. In this paper, we propose a generalized single-image-based MAD (S-MAD) algorithm by learning the encoding from Vision Transformer (ViT) architecture. Compared to CNN-based architectures, ViT model has the advantage on integrating local and global information and hence can be suitable to detect the morphing traces widely distributed among the face region. Extensive experiments are carried out on face morphing datasets generated using publicly available FRGC face datasets. Several state-of-the-art (SOTA) MAD algorithms, including representative ones that have been publicly evaluated, have been selected and benchmarked with our ViT-based approach. Obtained results demonstrate the improved detection performance of the proposed S-MAD method on inter-dataset testing (when different data is used for training and testing) and comparable performance on intra-dataset testing (when the same data is used for training and testing) experimental protocol.
Abstract (translated)
面部变形攻击已经对应用于边境控制和护照发放等场景的面部识别系统（FRS）构成了严重威胁。为此，需要开发能够防御此类攻击的面部变形检测算法（MAD）。这些算法必须足够稳健以处理开放集场景中的未知攻击，其中攻击可能源自多种不同的生成方法、后处理过程以及打印机/扫描仪的多样性。当仅基于一张疑似图像进行判断时，泛化问题更为突出。 本文提出了一种基于单张图片的通用面部变形检测（S-MAD）算法，并通过从视觉变压器（ViT）架构中学习编码来实现这一目标。与基于CNN的架构相比，ViT模型在整合局部和全局信息方面具有优势，因此更适于检测广泛分布在脸部区域中的变形痕迹。 我们在使用公开可用的FRGC面部数据集生成的脸部变形数据集上进行了广泛的实验，并将包括已公开评估在内的几种最先进的（SOTA）MAD算法与我们的基于ViT的方法进行了基准测试。结果表明，在跨数据集测试（即训练和测试使用的数据不同）时，所提出的S-MAD方法表现出改进的检测性能；在同数据集测试（即使用相同的数据进行训练和测试）时，则具有相当的检测性能。
URL
https://arxiv.org/abs/2501.09817
