  The need for reliable systems to determine fingerprint presentation attacks
grows with the rising use of the fingerprint for authentication. This work
presents a new approach to single-class classification for software-based
fingerprint presentation attach detection. The described method utilizes a
Wasserstein GAN to apply transfer learning to a deep convolutional autoencoder.
By doing so, the autoencoder could be pretrained and finetuned on the
LivDet2021 Dermalog sensor dataset with only 1122 bona fide training samples.
Without making use of any presentation attack samples, the model could archive
an average classification error rate of 16.79%. The Wasserstein GAN implemented
to pretrain the autoencoders weights can further be used to generate
realistic-looking artificial fingerprint patches. Extensive testing of
different autoencoder architectures and hyperparameters led to coarse
architectural guidelines as well as multiple implementations which can be
utilized for future work.
