2025 |
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 In: Proceedings of IEEE/CFV Winter Conference on Applications in Computer Vision - Workshops (WACV-W) 2025, pp. 1-11, Tucson, USA, 2025. Abstract | Links | BibTeX | Tags: 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. |
2023 |
Ivanovska, Marija; Štruc, Vitomir Face Morphing Attack Detection with Denoising Diffusion Probabilistic Models Proceedings Article In: Proceedings of the International Workshop on Biometrics and Forensics (IWBF), pp. 1-6, 2023. Abstract | Links | BibTeX | Tags: biometrics, deep learning, denoising diffusion probabilistic models, diffusion, face, face morphing attack, morphing attack, morphing attack detection @inproceedings{IWBF2023_Marija,Morphed face images have recently become a growing concern for existing face verification systems, as they are relatively easy to generate and can be used to impersonate someone's identity for various malicious purposes. Efficient Morphing Attack Detection (MAD) that generalizes well across different morphing techniques is, therefore, of paramount importance. Existing MAD techniques predominantly rely on discriminative models that learn from examples of bona fide and morphed images and, as a result, often exhibit sub-optimal generalization performance when confronted with unknown types of morphing attacks. To address this problem, we propose a novel, diffusion--based MAD method in this paper that learns only from the characteristics of bona fide images. Various forms of morphing attacks are then detected by our model as out-of-distribution samples. We perform rigorous experiments over four different datasets (CASIA-WebFace, FRLL-Morphs, FERET-Morphs and FRGC-Morphs) and compare the proposed solution to both discriminatively-trained and once-class MAD models. The experimental results show that our MAD model achieves highly competitive results on all considered datasets. |
2022 |
Ivanovska, Marija; Kronovšek, Andrej; Peer, Peter; Štruc, Vitomir; Batagelj, Borut Face Morphing Attack Detection Using Privacy-Aware Training Data Proceedings Article In: Proceedings of ERK 2022, pp. 1-4, 2022. Abstract | Links | BibTeX | Tags: 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. |