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Adversarial learning for a robust iris presentation attack detection method against unseen attack presentations
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Text/Conference Paper
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Datum
2019
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Verlag
Gesellschaft für Informatik e.V.
Zusammenfassung
Despite the high performance of current presentation attack detection (PAD) methods, the
robustness to unseen attacks is still an under addressed challenge. This work approaches the problem
by enforcing the learning of the bona fide presentations while making the model less dependent on
the presentation attack instrument species (PAIS). The proposed model comprises an encoder, mapping
from input features to latent representations, and two classifiers operating on these underlying
representations: (i) the task-classifier, for predicting the class labels (as bona fide or attack); and (ii)
the species-classifier, for predicting the PAIS. In the learning stage, the encoder is trained to help
the task-classifier while trying to fool the species-classifier. Plus, an additional training objective
enforcing the similarity of the latent distributions of different species is added leading to a ‘PAIspecies’-
independent model. The experimental results demonstrated that the proposed regularisation
strategies equipped the neural network with increased PAD robustness. The adversarial model obtained
better loss and accuracy as well as improved error rates in the detection of attack and bona
fide presentations.