Face anti-spoofing (FAS) based on domain generalization (DG) has been recently studied to improve the generalization on unseen scenarios. Previous methods typically rely on domain labels to align the distribution of each domain for learning domain-invariant representations. However, artificial domain labels are coarse-grained and subjective, which cannot reflect real domain distributions accurately. Besides, such domain-aware methods focus on domain-level alignment, which is not fine-grained enough to ensure that learned representations are insensitive to domain styles. To address these issues, we propose a novel perspective for DG FAS that aligns features on the instance level without the need for domain labels. Specifically, Instance-Aware Domain Generalization framework is proposed to learn the generalizable feature by weakening the features' sensitivity to instance-specific styles. Concretely, we propose Asymmetric Instance Adaptive Whitening to adaptively eliminate the style-sensitive feature correlation, boosting the generalization. Moreover, Dynamic Kernel Generator and Categorical Style Assembly are proposed to first extract the instance-specific features and then generate the style-diversified features with large style shifts, respectively, further facilitating the learning of style-insensitive features. Extensive experiments and analysis demonstrate the superiority of our method over state-of-the-art competitors. Code will be publicly available at https://github.com/qianyuzqy/IADG.

翻译：基于域泛化的人脸反欺骗技术近期被研究以提升在未知场景下的泛化能力。现有方法通常依赖域标签来对齐各域的分布，以学习域不变表示。然而，人工标注的域标签具有粗粒度性和主观性，无法准确反映真实域分布。此外，此类域感知方法聚焦于域级对齐，其精细度不足以确保所学表示对域风格不敏感。为解决这些问题，我们提出一种面向域泛化人脸反欺骗的新视角，无需域标签即可在实例级别对齐特征。具体而言，我们提出实例感知域泛化框架，通过削弱特征对实例特定风格的敏感性来学习可泛化特征。我们提出非对称实例自适应白化方法，自适应消除对风格敏感的特征关联，提升泛化能力。此外，我们提出动态核生成器与类别风格组装方法，分别用于提取实例特定特征并生成具有大风格偏移的多样化特征，进一步促进风格不敏感特征的学习。大量实验与分析表明，我们的方法优于现有最优方法。代码将开源于https://github.com/qianyuzqy/IADG。