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@inproceedings{MarijaFG2025,

title = {SelfMAD: Enhancing Generalization and Robustness in Morphing Attack Detection via Self-Supervised Learning},

author = {Marija Ivanovska and Leon Todorov and Naser Damer and Deepak Kumar Jain and Peter Peer and

Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2025/04/FG2025__SelfMAD.pdf

http://arxiv.org/abs/2504.05504},

year  = {2025},

date = {2025-05-27},

booktitle = {IEEE International Conference on Automatic Face and Gesture Recognition 2025},

pages = {1-10},

abstract = {With the continuous advancement of generative models, face morphing attacks have become a significant challenge for existing face verification systems due to their potential use in identity fraud and other malicious activities. Contemporary Morphing Attack Detection (MAD) approaches frequently rely on supervised, discriminative models trained on examples of bona fide and morphed images. These models typically perform well with morphs generated with techniques seen during training, but often lead to suboptimal performance when subjected to novel unseen morphing techniques. While unsupervised models have been shown to perform better in terms of generalizability, they typically result in higher error rates, as they struggle to effectively capture features of subtle artifacts. To address these shortcomings, we present SelfMAD, a novel self-supervised approach that simulates general morphing attack artifacts, allowing classifiers to learn generic and robust decision boundaries without overfitting to the specific artifacts induced by particular face morphing methods. Through extensive experiments on widely used datasets, we demonstrate that SelfMAD significantly outperforms current state-of-the-art MADs, reducing the detection error by more than 64% in terms of EER when compared to the strongest unsupervised competitor, and by more than 66%, when compared to the best performing discriminative MAD model, tested in cross-morph settings. The source code for SelfMAD is available at https://github.com/LeonTodorov/SelfMAD.},

keywords = {biometrics, face, face morphing, face morphing attack, face morphing detection, self-supervised learning, selfMAD},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{FadiWACV2025_Foundation,

title = {MADation: Face Morphing Attack Detection with Foundation Models},

author = {Eduarda Caldeira and Guray Ozgur and Tahar Chettaoui and Marija Ivanovska and Peter Peer and Fadi Boutros and Vitomir Struc and Naser Damer},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2025/01/MADation__Face_Morphing_Attack_Detection_with_Foundation_Models.pdf},

year  = {2025},

date = {2025-03-01},

booktitle = {Proceedings of IEEE/CFV Winter Conference on Applications in Computer Vision - Workshops (WACV-W) 2025},

pages = {1-11},

address = {Tucson, USA},

abstract = {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.},

keywords = {face morphing, face morphing attack, face morphing detection, foundation models, morphing attack, morphing attack detection},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{MarijaMorphing,

title = {Face Morphing Attack Detection Using Privacy-Aware Training Data},

author = {Marija Ivanovska and Andrej Kronovšek and Peter Peer and Vitomir Štruc and Borut Batagelj },

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2022/08/2022_ERK__Face_Morphing_Attack_Detecton_Using_Privacy_Aware_Training_Data.pdf},

year  = {2022},

date = {2022-08-01},

urldate = {2022-08-01},

booktitle = {Proceedings of ERK 2022},

pages = {1-4},

abstract = {Images of morphed faces pose a serious threat to face recognition--based security systems, as they can be used to illegally verify the identity of multiple people with a single morphed image. Modern detection algorithms learn to identify such morphing attacks using authentic images of real individuals. This approach raises various privacy concerns and limits the amount of publicly available training data. In this paper, we explore the efficacy of detection algorithms that are trained only on faces of non--existing people and their respective morphs. To this end, two dedicated algorithms are trained with synthetic data and then evaluated on three real-world datasets, i.e.: FRLL-Morphs, FERET-Morphs and FRGC-Morphs. Our results show that synthetic facial images can be successfully employed for the training process of the detection algorithms and generalize well to real-world scenarios.},

keywords = {competition, face, face morphing, face morphing attack, face morphing detection, private data, synthetic data},

pubstate = {published},

tppubtype = {inproceedings}

}