论文标题
矫形器：通过正交身份解散的变形攻击检测
OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement
论文作者
论文摘要
变形攻击是不断影响深度识别系统的众多威胁之一。它包括从不同个体中选择两个面，并将它们融合到包含两者的身份信息的最终图像中。在这项工作中，我们提出了一个新颖的正规化术语，该术语考虑了两者中存在的身份信息，并促进了两个正交潜在媒介的创建。我们在FRLL数据集中评估了我们提出的方法（Orthomad）的五种不同类型的变形，并在五个不同的数据集中培训时评估了模型的性能。以小型的RESNET-18为骨干，我们实现了大多数实验的最先进，并在其他实验中取得了竞争力。本文的代码将公开可用。
Morphing attacks are one of the many threats that are constantly affecting deep face recognition systems. It consists of selecting two faces from different individuals and fusing them into a final image that contains the identity information of both. In this work, we propose a novel regularisation term that takes into account the existent identity information in both and promotes the creation of two orthogonal latent vectors. We evaluate our proposed method (OrthoMAD) in five different types of morphing in the FRLL dataset and evaluate the performance of our model when trained on five distinct datasets. With a small ResNet-18 as the backbone, we achieve state-of-the-art results in the majority of the experiments, and competitive results in the others. The code of this paper will be publicly available.
