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 unlabeled 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

翻译：尽管近年来人脸识别算法的性能已取得显著提升，但推动这一进步的科学进展同样可用于构建攻击这些算法的有效手段，从而对其安全部署构成威胁。融合攻击检测系统旨在早期检测特定类型的威胁——融合攻击，防止其在关键流程中被用于身份验证。基础模型通过海量无标注数据进行学习，在未见领域展现出卓越的零样本泛化能力。虽然处理领域特定的下游任务时这种泛化能力可能较弱，但基础模型能够轻松适应这些场景，同时保留预训练阶段习得的内在知识。本研究认识到基础模型在适应特定任务需求后具备在融合攻击检测任务中取得优异表现的潜力。为此，我们采用LoRA权重适配CLIP架构的基础模型，并同步训练分类头模块。所提出的MADation框架超越了其他基于基础模型和Transformer的替代方案，成为首个将基础模型适配于融合攻击检测任务的研究。MADation在现有文献中的融合攻击检测解决方案中展现出竞争优势，并在多个评估场景中实现性能超越。为促进研究可复现性并推动融合攻击检测领域的后续探索，我们在https://github.com/gurayozgur/MADation公开发布了MADation的实现代码。