用可学习的变换参数生成对抗样本，提升虹膜活体检测跨域性能。
A Parametric Approach to Adversarial Augmentation for Cross-Domain Iris Presentation Attack Detection
- 通过卷积自编码器结合几何光照变换参数生成对抗样本。
- 在LivDet-Iris 2017和2020数据集上显著提升跨域检测准确率。
- 适合需要高鲁棒性虹膜识别系统的开发者参考。
基于虹膜的生物特征系统易受呈现攻击（如打印虹膜图像、纹理隐形眼镜）威胁。尽管已有多种呈现攻击检测（PAD）算法，但在跨域场景下表现不佳，因训练与测试使用不同传感器、攻击设备和数据集。本文提出一种基于参数化对抗增强的方法，利用经典数据增强中的平移、旋转等变换参数，通过卷积自编码器ADV-GEN生成对抗样本。该方法将变换参数作为正则化变量，引导生成过程在受限空间内进行。在包含四个数据集的LivDet-Iris 2017和LivDet-Iris 2020数据集上的实验验证了其有效性。代码已开源。
原文摘要 · Abstract (English)
Iris-based biometric systems are vulnerable to presentation attacks (PAs), where adversaries present physical artifacts (e.g., printed iris images, textured contact lenses) to defeat the system. This has led to the development of various presentation attack detection (PAD) algorithms, which typically perform well in intra-domain settings. However, they often struggle to generalize effectively in cross-domain scenarios, where training and testing employ different sensors, PA instruments, and datasets. In this work, we use adversarial training samples of both bonafide irides and PAs to improve the cross-domain performance of a PAD classifier. The novelty of our approach lies in leveraging transformation parameters from classical data augmentation schemes (e.g., translation, rotation) to generate adversarial samples. We achieve this through a convolutional autoencoder, ADV-GEN, that inputs original training samples along with a set of geometric and photometric transformations. The transformation parameters act as regularization variables, guiding ADV-GEN to generate adversarial samples in a constrained search space. Experiments conducted on the LivDet-Iris 2017 database, comprising four datasets, and the LivDet-Iris 2020 dataset, demonstrate the efficacy of our proposed method. The code is available at https://github.com/iPRoBe-lab/ADV-GEN-IrisPAD.
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