Konferenzbeitrag
Fake Face Detection Methods: Can They Be Generalized?
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Datum
2018
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Verlag
Köllen Druck+Verlag GmbH
Zusammenfassung
With advancements in technology, it is now possible to create representations of human
faces in a seamless manner for fake media, leveraging the large-scale availability of videos. These
fake faces can be used to conduct personation attacks on the targeted subjects. Availability of open
source software and a variety of commercial applications provides an opportunity to generate fake
videos of a particular target subject in a number of ways. In this article, we evaluate the generalizability
of the fake face detection methods through a series of studies to benchmark the detection
accuracy. To this extent, we have collected a new database of more than 53;000 images, from 150
videos, originating from multiple sources of digitally generated fakes including Computer Graphics
Image (CGI) generation and many tampering based approaches. In addition, we have also included
images (with more than 3;200) from the predominantly used Swap-Face application that is commonly
available on smart-phones. Extensive experiments are carried out using both texture-based
handcrafted detection methods and deep learning based detection methods to find the suitability
of detection methods. Through the set of evaluation, we attempt to answer if the current fake face
detection methods can be generalizable.