WaveSpec: Multi-Spectral Iris Presentation Attack Detection via Wavelength-Conditioned Spectral Coherence Adaptation

Raghavendra Ramachandra

Multispectral iris presentation attack detection benefits from observing the same iris across multiple near-infrared wavelengths, but existing methods often use wavelength information mainly for conditioning or fusion. This can limit their ability to explicitly model whether the spectral response of a sample is coherent across bands. In this paper, we propose \textbf{WaveSpec}, a multispectral iris PAD framework that performs feature-level spectral coherence adaptation. Each NIR band is first encoded with a DINOv2 backbone. The proposed Set Conditioned Spectral Coherence Adapter then updates each band feature using the consensus of the available spectral set, the band-specific deviation from that consensus, and wavelength identity. The adapted features are projected into structural, spectral, and anomaly representations, adaptively pooled, and classified as bona fide or attack. We evaluate WaveSpec on the MSIrPAD benchmark under a strict cross-artefact protocol, where the model is trained with one presentation attack instrument and tested on unseen attacks. WaveSpec reduces the average D-EER from 8.45\% for SpectraIrisPAD to 3.33\%, and reduces average BPCER@APCER=1\% from…