Iris presentation attack detection (PAD) has achieved great success under intra-domain settings but easily degrades on unseen domains. Conventional domain generalization methods mitigate the gap by learning domain-invariant features. However, they ignore the discriminative information in the domain-specific features. Moreover, we usually face a more realistic scenario with only one single domain available for training. To tackle the above issues, we propose a Single Domain Dynamic Generalization (SDDG) framework, which simultaneously exploits domain-invariant and domain-specific features on a per-sample basis and learns to generalize to various unseen domains with numerous natural images. Specifically, a dynamic block is designed to adaptively adjust the network with a dynamic adaptor. And an information maximization loss is further combined to increase diversity. The whole network is integrated into the meta-learning paradigm. We generate amplitude perturbed images and cover diverse domains with natural images. Therefore, the network can learn to generalize to the perturbed domains in the meta-test phase. Extensive experiments show the proposed method is effective and outperforms the state-of-the-art on LivDet-Iris 2017 dataset.

翻译：虹膜呈现攻击检测（PAD）在域内设置下取得了巨大成功，但在未见过的域上性能容易退化。传统的域泛化方法通过学习域不变特征来缩小差距，然而，它们忽略了域特定特征中的判别信息。此外，我们通常面临一个更现实的场景：仅有一个单域可用于训练。为解决上述问题，我们提出了一个单域动态泛化（SDDG）框架，该框架在逐样本基础上同时利用域不变和域特定特征，并学习利用大量自然图像泛化到各种未见过的域。具体而言，我们设计了一个动态块，通过动态适配器自适应地调整网络，并进一步结合信息最大化损失以增加多样性。整个网络集成到元学习范式之中。我们生成振幅扰动图像，并利用自然图像覆盖多样化的域，从而使网络能够在元测试阶段学习泛化到扰动域。大量实验表明，所提出的方法在LivDet-Iris 2017数据集上有效且优于当前最先进方法。