A Parametric Approach to Adversarial Augmentation for Cross-Domain Iris Presentation Attack Detection

Debasmita Pal, Redwan Sony, Arun Ross; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 5719-5729

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.