Emergent Morphing Attack Detection in Open Multi-modal Large Language Models

• URL: http://arxiv.org/abs/2602.15461v1
• Date: Tue, 17 Feb 2026 09:56:33 GMT
• Title: Emergent Morphing Attack Detection in Open Multi-modal Large Language Models
• Authors: Marija Ivanovska, Vitomir Štruc,
• Abstract summary: Face morphing attacks threaten biometric verification.<n>Most morphing attack detection (MAD) systems require task-specific training and generalize poorly to unseen attack types.<n>We present the first systematic zero-shot evaluation of open-source multimodal large language models (MLLMs) for single-image MAD.
• Score: 1.9620938589583623
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Face morphing attacks threaten biometric verification, yet most morphing attack detection (MAD) systems require task-specific training and generalize poorly to unseen attack types. Meanwhile, open-source multimodal large language models (MLLMs) have demonstrated strong visual-linguistic reasoning, but their potential in biometric forensics remains underexplored. In this paper, we present the first systematic zero-shot evaluation of open-source MLLMs for single-image MAD, using publicly available weights and a standardized, reproducible protocol. Across diverse morphing techniques, many MLLMs show non-trivial discriminative ability without any fine-tuning or domain adaptation, and LLaVA1.6-Mistral-7B achieves state-of-the-art performance, surpassing highly competitive task-specific MAD baselines by at least 23% in terms of equal error rate (EER). The results indicate that multimodal pretraining can implicitly encode fine-grained facial inconsistencies indicative of morphing artifacts, enabling zero-shot forensic sensitivity. Our findings position open-source MLLMs as reproducible, interpretable, and competitive foundations for biometric security and forensic image analysis. This emergent capability also highlights new opportunities to develop state-of-the-art MAD systems through targeted fine-tuning or lightweight adaptation, further improving accuracy and efficiency while preserving interpretability. To support future research, all code and evaluation protocols will be released upon publication.

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