  Morphing attacks is 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 face morphing attack detection is
developing rapidly, however very few datasets with several forms of attacks are
publicly available. This paper bridges this gap by providing a new dataset with
four different types of morphing attacks, based on OpenCV, FaceMorpher,
WebMorph and a generative adversarial network (StyleGAN), generated with
original face images from three public face datasets. We also conduct extensive
experiments to assess the vulnerability of the state-of-the-art face
recognition systems, notably FaceNet, VGG-Face, and ArcFace. The experiments
demonstrate that VGG-Face, while being less accurate face recognition system
compared to FaceNet, is also less vulnerable to morphing attacks. Also, we
observed that na\"ive morphs generated with a StyleGAN do not pose a
significant threat.
