DISI-Dipartimento di Informatica-Scienza e Ingegneria, Università di Bologna, 47521 Cesena, Italy.
Sensors (Basel). 2021 May 16;21(10):3466. doi: 10.3390/s21103466.
Face morphing and related morphing attacks have emerged as a serious security threat for automatic face recognition systems and a challenging research field. Therefore, the availability of effective and reliable morphing attack detectors is strongly needed. In this paper, we proposed a framework based on a double Siamese architecture to tackle the morphing attack detection task in the differential scenario, in which two images, a trusted live acquired image and a probe image (morphed or bona fide) are given as the input for the system. In particular, the presented framework aimed to merge the information computed by two different modules to predict the final score. The first one was designed to extract information about the identity of the input faces, while the second module was focused on the detection of artifacts related to the morphing process. Experimental results were obtained through several and rigorous cross-dataset tests, exploiting three well-known datasets, namely PMDB, MorphDB, and AMSL, containing automatic and manually refined facial morphed images, showing that the proposed framework was able to achieve satisfying results.
人脸变形和相关的变形攻击已经成为自动人脸识别系统的严重安全威胁，也是一个具有挑战性的研究领域。因此，强烈需要有效的、可靠的变形攻击探测器。在本文中，我们提出了一个基于双暹罗网络架构的框架，以解决差分场景中的变形攻击检测任务，其中系统的输入是两个图像，一个可信的活体采集图像和一个探针图像（变形或真实的）。特别地，所提出的框架旨在合并由两个不同模块计算的信息以预测最终分数。第一个模块旨在提取关于输入人脸身份的信息，而第二个模块则专注于检测与变形过程相关的伪影。通过在三个知名数据集（即 PMDB、MorphDB 和 AMSL）上进行多次严格的跨数据集测试，获得了实验结果，这些数据集包含自动和手动细化的人脸变形图像，表明所提出的框架能够取得令人满意的结果。
