论文标题
使用暹罗网络和少量学习的单个变形攻击检测
Single Morphing Attack Detection using Siamese Network and Few-shot Learning
论文作者
论文摘要
面部变形攻击检测具有挑战性，并为面部验证系统带来了具体和严重的威胁。此类攻击的可靠检测机制，已通过强大的跨数据库协议和未知的变形工具进行了测试，仍然是一项研究挑战。本文提出了一个框架，遵循了几次射击学习方法，该框架使用Triplet-Semi-Hard-loss基于暹罗网络共享图像信息，以解决变形攻击检测并增强聚类分类过程。该网络将真正的形态图像与形变和真正的面部图像进行比较。我们的结果表明，这个新的网络将数据点群集成，并将它们分配给类，以便在跨数据库方案中获得较低的相等错误率，仅共享来自未知数据库的小图像编号。几乎没有学习的学习有助于增强学习过程。使用FRGCV2训练并使用FERET和AMSL开放式数据库测试的跨数据库的实验结果将BPCER10使用RESNET50和5.50％的MobileNETV2降低至4.91％。
Face morphing attack detection is challenging and presents a concrete and severe threat for face verification systems. Reliable detection mechanisms for such attacks, which have been tested with a robust cross-database protocol and unknown morphing tools still is a research challenge. This paper proposes a framework following the Few-Shot-Learning approach that shares image information based on the siamese network using triplet-semi-hard-loss to tackle the morphing attack detection and boost the clustering classification process. This network compares a bona fide or potentially morphed image with triplets of morphing and bona fide face images. Our results show that this new network cluster the data points, and assigns them to classes in order to obtain a lower equal error rate in a cross-database scenario sharing only small image numbers from an unknown database. Few-shot learning helps to boost the learning process. Experimental results using a cross-datasets trained with FRGCv2 and tested with FERET and the AMSL open-access databases reduced the BPCER10 from 43% to 4.91% using ResNet50 and 5.50% for MobileNetV2.
