Quality-driven Adaptive Morphing Attack Detection in Operational Scenarios via Online Learning

Morphing Attack Detection (MAD) systems often suffer from performance degradation when deployed in operational environments, such as airports, that differ from the training domain. We propose an adaptive differential MAD framework that continuously refines a pre-trained detector using live bona fide samples acquired at the gate. The system is memoryless, so no samples are stored in memory to mitigate privacy concerns about the collection of personal data. To prevent the loss of discriminative power caused by bona fide-only adaptation, the method generates synthetic morph samples on-the-fly by combining the current operational subject with identities from an external public or synthetic face dataset. The adaptation process further relies on a quality-aware bona fide selection strategy and a controlled balancing mechanism for synthetic morph generation. Experimental results show that the proposed method improves target-domain specialization while maintaining robustness against morph attacks.