  This paper proposes a framework for a privacy-safe iris presentation attack
detection (PAD) method, designed solely with synthetically-generated,
identity-leakage-free iris images. Once trained, the method is evaluated in a
classical way using state-of-the-art iris PAD benchmarks. We designed two
generative models for the synthesis of ISO/IEC 19794-6-compliant iris images.
The first model synthesizes bona fide-looking samples. To avoid ``identity
leakage,'' the generated samples that accidentally matched those used in the
model's training were excluded. The second model synthesizes images of irises
with textured contact lenses and is conditioned by a given contact lens brand
to have better control over textured contact lens appearance when forming the
training set. Our experiments demonstrate that models trained solely on
synthetic data achieve a lower but still reasonable performance when compared
to solutions trained with iris images collected from human subjects. This is
the first-of-its-kind attempt to use solely synthetic data to train a
fully-functional iris PAD solution, and despite the performance gap between
regular and the proposed methods, this study demonstrates that with the
increasing fidelity of generative models, creating such privacy-safe iris PAD
methods may be possible. The source codes and generative models trained for
this work are offered along with the paper.
