  Morphing attacks are a threat to biometric systems where the biometric
reference in an identity document can be altered. This form of attack presents
an important issue in applications relying on identity documents such as border
security or access control. Research in generation of face morphs and their
detection is developing rapidly, however very few datasets with morphing
attacks and open-source detection toolkits are publicly available. This paper
bridges this gap by providing two datasets and the corresponding code for four
types of morphing attacks: two that rely on facial landmarks based on OpenCV
and FaceMorpher, and two that use StyleGAN 2 to generate synthetic morphs. We
also conduct extensive experiments to assess the vulnerability of four
state-of-the-art face recognition systems, including FaceNet, VGG-Face,
ArcFace, and ISV. Surprisingly, the experiments demonstrate that, although
visually more appealing, morphs based on StyleGAN 2 do not pose a significant
threat to the state to face recognition systems, as these morphs were
outmatched by the simple morphs that are based facial landmarks.
