Compact Vision Models for Iris Presentation Attack Detection under Presentation Attack Instrument Shift and Environmental Degradation
Compact iris presentation-attack detection models under 0.3M parameters show APCER rising 17-30 percentage points against unseen attack instruments, indicating limited deployment readiness.
Three scratch-trained compact vision models, each with at most roughly 0.26 million trainable parameters, were benchmarked on the Notre Dame subset of LivDet-Iris 2017 under presentation attack instrument shift and environmental degradation, trained without pretraining or augmentation and evaluated over five seeds. Moving from known to unknown attack presentations raises APCER by 17.11-30.47 percentage points and D-EER by 7.38-12.73 points. ZACH-ViT achieves the lowest unknown-attack APCER (47.69% +/- 4.84%) and D-EER (38.87% +/- 0.93%), while Compact-TransMIL yields the lowest bona fide classification error rate; high absolute errors show none are deployment-ready.
- Three compact models, at most ~0.26M parameters, trained from scratch without pretraining or augmentation.
- Unknown-attack APCER rises 17.11-30.47 percentage points versus known attacks on LivDet-Iris 2017 Notre Dame data.
- ZACH-ViT achieves the lowest unknown-attack APCER of 47.69% and D-EER of 38.87%.
- High absolute error rates show no compact model is deployment-ready under unknown presentation attack instruments.
- Organizations
- University of Notre Dame
From the original article
1 min read at arxiv.orgIris presentation attack detection (PAD) is security-critical when a subsystem that appears reliable during development encounters presentation attack instruments (PAIs) or acquisition conditions absent from validation data. We benchmark three compact scratch-trained computer-vision models, each with at most approximately 0.
By Athanasios Angelakis · arxiv.org
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