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Morphing attacks is a threat to biometric systems where the biometricreference in an identity document can be altered. This form of attack presentsan important issue in applications relying on identity documents such as bordersecurity or access control. Research in face morphing attack detection isdeveloping rapidly, however very few datasets with several forms of attacks arepublicly available. This paper bridges this gap by providing a new dataset withfour different types of morphing attacks, based on OpenCV, FaceMorpher,WebMorph and a generative adversarial network (StyleGAN), generated withoriginal face images from three public face datasets. We also conduct extensiveexperiments to assess the vulnerability of the state-of-the-art facerecognition systems, notably FaceNet, VGG-Face, and ArcFace. The experimentsdemonstrate that VGG-Face, while being less accurate face recognition systemcompared to FaceNet, is also less vulnerable to morphing attacks. Also, weobserved that na\"ive morphs generated with a StyleGAN do not pose asignificant threat.

Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks