Abstract
Morphing attack detection has become an essential component of face recognition systems for ensuring a reliable verification scenario. In this paper, we present a multimodal learning approach that can provide a textual description of morphing attack detection. We first show that zero-shot evaluation of the proposed framework using Contrastive Language-Image Pretraining (CLIP) can yield not only generalizable morphing attack detection, but also predict the most relevant text snippet. We present an extensive analysis of ten different textual prompts that include both short and long textual prompts. These prompts are engineered by considering the human understandable textual snippet. Extensive experiments were performed on a face morphing dataset that was developed using a publicly available face biometric dataset. We present an evaluation of SOTA pre-trained neural networks together with the proposed framework in the zero-shot evaluation of five different morphing generation techniques that are captured in three different mediums.
Abstract (translated)
面部变形攻击检测已成为确保面部识别系统可靠验证场景的重要组成部分。本文提出了一种多模态学习方法，可以为变形攻击检测提供文本描述。首先展示使用对比语言-图像预训练（CLIP）对所提框架进行零样本评估不仅可以实现泛化的变形攻击检测，还能预测最相关的文本片段。我们详细分析了十种不同的文本提示，包括短文本和长文本提示，这些提示是根据人类可理解的文本片段设计的。在使用公开可用的人脸生物识别数据集开发的脸部变形数据集上进行了大量实验。我们在零样本评估中对五种不同生成技术（以三种不同媒介捕获）的最先进预训练神经网络以及所提出的框架进行了一项评价。 该研究揭示了通过对比语言-图像预训练模型能够有效实现面部变形攻击检测，并且可以输出相应的文本描述，进一步提高系统的解释性和实用性。
URL
https://arxiv.org/abs/2508.10110
