The vulnerability of face recognition systems against so-called morphing attacks has been revealed in the past years. Recently, different kinds of morphing attack detection approaches have been proposed. However, the vast majority of published results has been obtained from rather constrained experimental setups. In particular, most investigations do not consider variations in morphing techniques, image sources, and image post-processing. Hence, reported performance rates can not be maintained in realistic scenarios, as the NIST FRVT MORPH performance evaluation showed. In this work, existing algorithms are benchmarked on a new, more realistic database. This database consists of two different data sets, from which morphs were created using four different morphing algorithms. In addition, the database contains four different post-processings (including print-scan transformation and JPEG2000 compression). Further, a new morphing attack detection method based on a fusion of different configurations of Multi-scale Block Local Binary Patterns (MB-LBP) on an image divided into multiple cells is presented. The proposed score-level fusion of a maximum number of 18 different configurations is shown to significantly improve the robustness of the resulting morphing attack detection scheme, yielding an average performance between 2.26% and 8.52% in terms of Detection Equal Error Rate (D-EER), depending on the applied post-processing.

中文翻译：

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未知变形算法和图像源的面部变形检测：多尺度块局部二进制模式融合方法

在过去的几年中，人脸识别系统针对所谓的变形攻击的脆弱性已经被揭示出来。最近，已经提出了不同种类的变态攻击检测方法。但是，绝大多数已发表的结果是从相当有限的实验设置中获得的。特别是，大多数研究都没有考虑变形技术，图像源和图像后处理的变化。因此，如NIST FRVT MORPH性能评估所示，在现实情况下无法保持报告的性能。在这项工作中，现有算法在新的，更实际的数据库中进行了基准测试。该数据库由两个不同的数据集组成，使用四种不同的变形算法从中创建了变形。此外，该数据库包含四个不同的后处理（包括打印扫描转换和JPEG2000压缩）。此外，提出了一种新的变态攻击检测方法，该方法基于在划分为多个单元的图像上融合多尺度块局部二进制模式（MB-LBP）的不同配置。所建议的最多18种不同配置的得分级融合可显着提高所得变态攻击检测方案的鲁棒性，从而在检测均等错误率（D-EER）方面产生2.26％至8.52％的平均性能。 ），具体取决于所应用的后处理。提出了一种新的变态攻击检测方法，该方法基于在分割成多个单元的图像上融合多尺度块局部二进制模式（MB-LBP）的不同配置。所建议的最多18种不同配置的得分级融合可显着提高所得变态攻击检测方案的鲁棒性，从而在检测均等错误率（D-EER）方面产生2.26％至8.52％的平均性能。 ），具体取决于所应用的后处理。提出了一种新的变态攻击检测方法，该方法基于在分割成多个单元的图像上融合多尺度块局部二进制模式（MB-LBP）的不同配置。所建议的最多18种不同配置的得分级融合可显着提高所得变态攻击检测方案的鲁棒性，在检测均等错误率（D-EER）方面，平均性能介于2.26％和8.52％之间），具体取决于所应用的后处理。