用多光谱图像提升虹膜伪造检测能力，效果优于现有方法。
SpectraIrisPAD: Leveraging Vision Foundation Models for Spectrally Conditioned Multispectral Iris Presentation Attack Detection
- 基于DINOv2的视觉模型，结合可学习波段位置编码与对比学习。
- 在5个近红外波段上检测18,848张图像中的8类伪造攻击，准确率更高。
- 新数据集含多种伪造类型，适合安全系统研发人员参考。
虹膜识别是目前最精确的生物特征识别方式之一，但其在真实场景中的广泛应用引发了对呈现攻击（PA）的担忧。有效的呈现攻击检测（PAD）对保障虹膜系统的完整性至关重要。传统虹膜系统主要工作在近红外（NIR）波段，而跨多个NIR波段的多光谱成像可提供互补的反射信息，有助于提升PAD方法的泛化能力。本文提出SpectraIrisPAD，一种基于深度学习的鲁棒多光谱虹膜PAD框架。该方法采用配备可学习光谱位置编码、令牌融合和对比学习的DINOv2视觉变换器骨干网络，提取具有判别性的波段特异性特征，有效区分真实样本与各类伪造物。此外，我们构建了新的多光谱虹膜PAD数据集MSIrPAD，使用定制的多光谱虹膜传感器在五个不同近红外波长（800 nm、830 nm、850 nm、870 nm、980 nm）下采集，包含18,848张图像，涵盖八类伪造攻击，包括五种带纹理隐形眼镜、打印攻击和显示攻击。我们在未见攻击评估协议下进行全面实验，结果表明SpectraIrisPAD在所有性能指标上均持续优于多个先进基线，展现出在检测多种呈现攻击时出色的鲁棒性和泛化能力。
原文摘要 · Abstract (English)
Iris recognition is widely recognized as one of the most accurate biometric modalities. However, its growing deployment in real-world applications raises significant concerns regarding its vulnerability to Presentation Attacks (PAs). Effective Presentation Attack Detection (PAD) is therefore critical to ensure the integrity and security of iris-based biometric systems. While conventional iris recognition systems predominantly operate in the near-infrared (NIR) spectrum, multispectral imaging across multiple NIR bands provides complementary reflectance information that can enhance the generalizability of PAD methods. In this work, we propose \textbf{SpectraIrisPAD}, a novel deep learning-based framework for robust multispectral iris PAD. The SpectraIrisPAD leverages a DINOv2 Vision Transformer (ViT) backbone equipped with learnable spectral positional encoding, token fusion, and contrastive learning to extract discriminative, band-specific features that effectively distinguish bona fide samples from various spoofing artifacts. Furthermore, we introduce a new comprehensive dataset Multispectral Iris PAD (\textbf{MSIrPAD}) with diverse PAIs, captured using a custom-designed multispectral iris sensor operating at five distinct NIR wavelengths (800\,nm, 830\,nm, 850\,nm, 870\,nm, and 980\,nm). The dataset includes 18,848 iris images encompassing eight diverse PAI categories, including five textured contact lenses, print attacks, and display-based attacks. We conduct comprehensive experiments under unseen attack evaluation protocols to assess the generalization capability of the proposed method. SpectraIrisPAD consistently outperforms several state-of-the-art baselines across all performance metrics, demonstrating superior robustness and generalizability in detecting a wide range of presentation attacks.
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