  Face-morphing attacks have been a cause for concern for a number of years.
Striving to remain one step ahead of attackers, researchers have proposed many
methods of both creating and detecting morphed images. These detection methods,
however, have generally proven to be inadequate. In this work we identify two
new, GAN-based methods that an attacker may already have in his arsenal. Each
method is evaluated against state-of-the-art facial recognition (FR) algorithms
and we demonstrate that improvements to the fidelity of FR algorithms do lead
to a reduction in the success rate of attacks provided morphed images are
considered when setting operational acceptance thresholds.
