  Though recent studies have made significant progress in morph attack
detection by virtue of deep neural networks, they often fail to generalize well
to unseen morph attacks. With numerous morph attacks emerging frequently,
generalizable morph attack detection has gained significant attention. This
paper focuses on enhancing the generalization capability of morph attack
detection from the perspective of consistency regularization. Consistency
regularization operates under the premise that generalizable morph attack
detection should output consistent predictions irrespective of the possible
variations that may occur in the input space. In this work, to reach this
objective, two simple yet effective morph-wise augmentations are proposed to
explore a wide space of realistic morph transformations in our consistency
regularization. Then, the model is regularized to learn consistently at the
logit as well as embedding levels across a wide range of morph-wise augmented
images. The proposed consistency regularization aligns the abstraction in the
hidden layers of our model across the morph attack images which are generated
from diverse domains in the wild. Experimental results demonstrate the superior
generalization and robustness performance of our proposed method compared to
the state-of-the-art studies.
