2026 |
Marić, Nikola; Ivanovska, Marija; Štruc, Vitomir Exploring Multimodal Large Language Models for Morphing Attack Detection Proceedings Article V: Proceedings of the 29th Computer Vision Winter Workshop, str. 1-10, 2026. Povzetek | Povezava | BibTeX | Oznake: biometrics, face analysis, face morphing, face morphing attack, face morphing detection, media forensics @inproceedings{CVWW2026,Existing single-image morphing attack detection (S-MAD) systems often suffer from poor cross-dataset generalization and operate as opaque “black boxes,” which is particularly problematic in high-stakes border control scenarios. This paper investigates the adoption of opensource Multimodal Large Language Models (MLLMs) for S-MAD under strict cross-dataset evaluation through two different approaches. First, we assess selected MLLMs in zero-shot settings using a structured forensic prompting framework, which elicits multi-step semantic analysis with human-readable regional attributions. Second, leveraging the lightweight and parameter-efficient LoRA approach and a synthetic training dataset of morphs, we adapt the best-performing MLLM to the morphing attack detection (MAD) task in an efficient, generalizable, and privacy-preserving manner, enhancing the model’s sensitivity to diverse morphing artifacts. Our experimental results show that the proposed prompting strategy significantly improves overall attack detection accuracy compared to naive prompting. Moreover, our LoRA-adapted MLLM, Gemma-3 12B, achieves an average equal error rate (EER) of 14.81% across various morphing attack benchmarks, outperforming widely used MAD models. |
2025 |
Ivanovska, Marija; Todorov, Leon; Damer, Naser; Jain, Deepak Kumar; Peer, Peter; Štruc, Vitomir SelfMAD: Enhancing Generalization and Robustness in Morphing Attack Detection via Self-Supervised Learning Proceedings Article V: IEEE International Conference on Automatic Face and Gesture Recognition 2025, str. 1-10, 2025. Povzetek | Povezava | BibTeX | Oznake: biometrics, face, face morphing, face morphing attack, face morphing detection, self-supervised learning, selfMAD @inproceedings{MarijaFG2025,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. |
Caldeira, Eduarda; Ozgur, Guray; Chettaoui, Tahar; Ivanovska, Marija; Peer, Peter; Boutros, Fadi; Struc, Vitomir; Damer, Naser MADation: Face Morphing Attack Detection with Foundation Models Proceedings Article V: Proceedings of IEEE/CFV Winter Conference on Applications in Computer Vision - Workshops (WACV-W) 2025, str. 1-11, Tucson, USA, 2025. Povzetek | Povezava | BibTeX | Oznake: face morphing, face morphing attack, face morphing detection, foundation models, morphing attack, morphing attack detection @inproceedings{FadiWACV2025_Foundation,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. |
2022 |
Ivanovska, Marija; Kronovšek, Andrej; Peer, Peter; Štruc, Vitomir; Batagelj, Borut Face Morphing Attack Detection Using Privacy-Aware Training Data Proceedings Article V: Proceedings of ERK 2022, str. 1-4, 2022. Povzetek | Povezava | BibTeX | Oznake: competition, face, face morphing, face morphing attack, face morphing detection, private data, synthetic data @inproceedings{MarijaMorphing,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. |
Objave
2026 |
Exploring Multimodal Large Language Models for Morphing Attack Detection Proceedings Article V: Proceedings of the 29th Computer Vision Winter Workshop, str. 1-10, 2026. |
2025 |
SelfMAD: Enhancing Generalization and Robustness in Morphing Attack Detection via Self-Supervised Learning Proceedings Article V: IEEE International Conference on Automatic Face and Gesture Recognition 2025, str. 1-10, 2025. |
MADation: Face Morphing Attack Detection with Foundation Models Proceedings Article V: Proceedings of IEEE/CFV Winter Conference on Applications in Computer Vision - Workshops (WACV-W) 2025, str. 1-11, Tucson, USA, 2025. |
2022 |
Face Morphing Attack Detection Using Privacy-Aware Training Data Proceedings Article V: Proceedings of ERK 2022, str. 1-4, 2022. |