MADation: Face Morphing Attack Detection with Foundation Models

Eduarda Caldeira, Guray Ozgur, Tahar Chettaoui, Marija Ivanovska, Peter Peer, Fadi Boutros, Vitomir Struc, Naser Damer; Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops, 2025, pp. 1650-1660

Despite the considerable performance improvements of face recognition algorithms in recent years the same scientific advances responsible for this progress can also be used to create efficient ways to attack them posing a threat to their secure deployment. Morphing attack detection (MAD) systems aim to detect a specific type of threat morphing attacks at an early stage preventing them from being considered for verification in critical processes. Foundation models (FM) learn from extensive amounts of unlabelled data achieving remarkable zero-shot generalization to unseen domains. Although this generalization capacity might be weak when dealing with domain-specific downstream tasks such as MAD FMs can easily adapt to these settings while retaining the built-in knowledge acquired during pre-training. In this work we recognize the potential of FMs to perform well in the MAD task when properly adapted to its specificities. To this end we adapt FM CLIP architectures with LoRA weights while simultaneously training a classification header. The proposed framework MADation surpasses our alternative FM and transformer-based frameworks and constitutes the first adaption of FMs to the MAD task. MADation presents competitive results with current MAD solutions in the literature and even surpasses them in several evaluation scenarios. To encourage reproducibility and facilitate further research in MAD we publicly release the implementation of MADation at https://github.com/gurayozgur/MADation