  Although a substantial amount of studies is dedicated to morph detection,
most of them fail to generalize for morph faces outside of their training
paradigm. Moreover, recent morph detection methods are highly vulnerable to
adversarial attacks. In this paper, we intend to learn a morph detection model
with high generalization to a wide range of morphing attacks and high
robustness against different adversarial attacks. To this aim, we develop an
ensemble of convolutional neural networks (CNNs) and Transformer models to
benefit from their capabilities simultaneously. To improve the robust accuracy
of the ensemble model, we employ multi-perturbation adversarial training and
generate adversarial examples with high transferability for several single
models. Our exhaustive evaluations demonstrate that the proposed robust
ensemble model generalizes to several morphing attacks and face datasets. In
addition, we validate that our robust ensemble model gain better robustness
against several adversarial attacks while outperforming the state-of-the-art
studies.
