Single Morphing Attack Detection using Siamese Network and Few-shot Learning
This addresses a severe threat to face verification systems, offering improved detection with incremental advancements in few-shot learning for morphing attacks.
The paper tackled the problem of detecting face morphing attacks in verification systems by proposing a few-shot learning framework with a Siamese network and triplet loss, achieving a reduction in BPCER10 from 43% to 4.91% using ResNet50 in cross-database tests.
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.