Transformers for Iris Presentation Attack Detection: Effectiveness and Behavior under Image Compression

As biometric recognition systems become increasingly prevalent in security-critical applications, ensuring their robustness against attacks is paramount to maintaining trust and reliability. This is particularly important for systems relying on highly accurate biometric traits such as the iris, for which malicious users may attempt to gain unauthorized access by resorting to artifacts or synthetic samples, i.e., through presentation attacks. This paper explores the capabilities of two state-of-the-art attention-based architectures, namely vision transformers (ViTs) and shifted windows (Swin) transformers, for iris presentation attack detection (PAD). Furthermore, a detailed analysis is performed on a relatively underexplored aspect, specifically the effects of image compression (JPEG and JPEG AI) on data-driven PAD. The obtained results testify to the effectiveness of transformer-based PAD frameworks, whose behaviors can be interpreted by exploring visual maps under degraded image quality. The trade-off between compression efficiency and PAD accuracy is also analyzed.