Towards Generalizable Morph Attack Detection with Consistency Regularization

TL;DR

This paper introduces a consistency regularization approach with morph-wise augmentations to improve the generalization of morph attack detection models against unseen morph attacks, demonstrating superior robustness.

Contribution

It proposes a novel consistency regularization framework using morph-wise augmentations to enhance generalization in morph attack detection.

Findings

Outperforms state-of-the-art methods in generalization

Achieves higher robustness against unseen morph attacks

Effective at both logit and embedding levels

Abstract

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.