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National Repository of Grey Literature 1 records found  Search took 0.00 seconds. 
Face Anti-Spoofing with Out-of-distribution Detection
 Češka, Petr ; Vaško, Marek (referee) ; Špaňhel, Jakub (advisor)
This thesis aims to improve the accuracy of Vision Transformer-based face anti-spoofing models in detecting presentation attacks. The thesis uses out-of-distribution detection to filter out images that are too different from the training data, referred to as in-distribution. It examines how successful different methods are in identifying different data distributions, and how the filtering of out-of-distribution data based on these methods affects the accuracy of the model. Using the relative Mahalanobis distance, an AUROC of 97.6% can be achieved when distinguishing between in-distribution and out-of-distribution data. Filtering out images that should not be classified increases the accuracy of all tested models to over 99.9%. This can provide an additional layer of security for applications against face spoofing attacks.
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