Abstract
Face Morphing Attack Detection (MAD) is a critical challenge in face recognition security, where attackers can fool systems by interpolating the identity information of two or more individuals into a single face image, resulting in samples that can be verified as belonging to multiple identities by face recognition systems. While multimodal foundation models (FMs) like CLIP offer strong zero-shot capabilities by jointly modeling images and text, most prior works on FMs for biometric recognition have relied on fine-tuning for specific downstream tasks, neglecting their potential for direct, generalizable deployment. This work explores a pure zero-shot approach to MAD by leveraging CLIP without any additional training or fine-tuning, focusing instead on the design and aggregation of multiple textual prompts per class. By aggregating the embeddings of diverse prompts, we better align the model's internal representations with the MAD task, capturing richer and more varied cues indicative of bona-fide or attack samples. Our results show that prompt aggregation substantially improves zero-shot detection performance, demonstrating the effectiveness of exploiting foundation models' built-in multimodal knowledge through efficient prompt engineering.
Abstract (translated)
面部变形攻击检测（MAD）是人脸识别安全中的一个关键挑战。在这种情况下，攻击者可以通过将两个或更多个人的身份信息插值到一张人脸图像中来欺骗系统，从而生成可以被验证为属于多个身份的人脸识别样本。虽然多模态基础模型（如CLIP）通过共同建模图像和文本提供了强大的零样本能力，但大多数先前关于生物特征识别中的FMs的工作依赖于针对特定下游任务的微调，而忽略了它们直接通用部署的潜力。这项工作探索了一种纯零样本方法来解决MAD问题，即在不进行任何额外训练或微调的情况下利用CLIP，而是专注于每个类别的多种文本提示的设计和聚合。通过聚合多样化的提示嵌入，我们可以更好地将模型内部表示与MAD任务对齐，捕捉更多更丰富的指示真实样本或攻击样本的线索。 我们的实验结果表明，提示聚合显著提高了零样本检测性能，证明了通过高效的提示工程来利用基础模型内置的多模态知识的有效性。
URL
https://arxiv.org/abs/2508.08939
