Synthetic Periocular Iris PAI from a Small Set of Near-Infrared-Images

TL;DR

This paper introduces a method to generate synthetic periocular iris presentation attack instruments using GANs, revealing that current PAD algorithms can be fooled by such synthetic images, highlighting the need for more robust detection methods.

Contribution

The paper proposes a novel approach to create synthetic PAI using multiple GAN algorithms from a small set of NIR images, demonstrating the potential to deceive PAD systems.

Findings

Synthetic images can fool PAD algorithms, categorizing them as bona fide.

StyleGAN2 produced the most realistic synthetic PAI among tested GANs.

Current PAD algorithms need to be updated with diverse training data.

Abstract

Biometric has been increasing in relevance these days since it can be used for several applications such as access control for instance. Unfortunately, with the increased deployment of biometric applications, we observe an increase of attacks. Therefore, algorithms to detect such attacks (Presentation Attack Detection (PAD)) have been increasing in relevance. The LivDet-2020 competition which focuses on Presentation Attacks Detection (PAD) algorithms have shown still open problems, specially for unknown attacks scenarios. In order to improve the robustness of biometric systems, it is crucial to improve PAD methods. This can be achieved by augmenting the number of presentation attack instruments (PAI) and bona fide images that are used to train such algorithms. Unfortunately, the capture and creation of presentation attack instruments and even the capture of bona fide images is sometimes complex to achieve. This paper proposes a novel PAI synthetically created (SPI-PAI) using four state-of-the-art GAN algorithms (cGAN, WGAN, WGAN-GP, and StyleGAN2) and a small set of periocular NIR images. A benchmark between GAN algorithms is performed using the Frechet Inception Distance (FID) between the generated images and the original images used for training. The best PAD algorithm reported by the LivDet-2020 competition was tested for us using the synthetic PAI which was obtained with the StyleGAN2 algorithm. Surprisingly, The PAD algorithm was not able to detect the synthetic images as a Presentation Attack, categorizing all of them as bona fide. Such results demonstrated the feasibility of synthetic images to fool presentation attacks detection algorithms and the need for such algorithms to be constantly updated and trained with a larger number of images and PAI scenarios.