Robust Ensemble Morph Detection with Domain Generalization
This addresses the security issue of face morph detection for biometric systems, but it is incremental as it builds on existing ensemble and adversarial training techniques.
The paper tackles the problem of morph detection models failing to generalize to unseen morphing attacks and being vulnerable to adversarial attacks, resulting in a robust ensemble model that generalizes to multiple morphing attacks and datasets while outperforming state-of-the-art methods in robustness.
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.