  We present the task of differential face morph attack detection using a
conditional generative network (cGAN). To determine whether a face image in an
identification document, such as a passport, is morphed or not, we propose an
algorithm that learns to implicitly disentangle identities from the morphed
image conditioned on the trusted reference image using the cGAN. Furthermore,
the proposed method can also recover some underlying information about the
second subject used in generating the morph. We performed experiments on AMSL
face morph, MorGAN, and EMorGAN datasets to demonstrate the effectiveness of
the proposed method. We also conducted cross-dataset and cross-attack detection
experiments. We obtained promising results of 3% BPCER @ 10% APCER on
intra-dataset evaluation, which is comparable to existing methods; and 4.6%
BPCER @ 10% APCER on cross-dataset evaluation, which outperforms
state-of-the-art methods by at least 13.9%.
