Fake Face Detection Methods: Can They Be Generalized?
Abstract
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 smartphones. 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.
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The Fake Faces in the Wild (FFW) dataset contains 131,500 face images, including 78,500 bona fide images from 150 videos in the FaceForensics dataset and 53,000 fake images from 150 fake YouTube videos.
“Fake Faces in the Wild (FFW) dataset (Khodabakhsh et al., 2018): This dataset contains 131,500 face images, including 78,500 bona fide images extracted from 150 videos in the FaceForensics dataset and 53,000 fake images extracted from 150 fake videos collected from YouTube.”
From Deepfake: definitions, performance metrics and standards, datasets, and a meta-review (2024) · cites this paper as Khodabakhsh et al., 2018
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- Authors
- Khodabakhsh, A., Ramachandra, R., Raja, K., Wasnik, P., & Busch, C.
- Affiliations
- Department of Information Security and Communication Technology, Norwegian University of Science and Technology, Gjovik, Norway
- Venue
- 2018 International Conference of the Biometrics Special Interest Group (BIOSIG)
- Publisher
- IEEE
- Published
- 2018-09
- Pages
- 1-6
- Cited by
- 108
- References
- 5
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