Article
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Fast Fake – easy to train Face Swap model
Version 1
: Received: 5 February 2024 / Approved: 5 February 2024 / Online: 5 February 2024 (12:29:12 CET)
A peer-reviewed article of this Preprint also exists.
Walczyna, T.; Piotrowski, Z. Fast Fake: Easy-to-Train Face Swap Model. Appl. Sci. 2024, 14, 2149. Walczyna, T.; Piotrowski, Z. Fast Fake: Easy-to-Train Face Swap Model. Appl. Sci. 2024, 14, 2149.
Abstract
"Deep fake" technologies are often associated with face-swapping algorithms based on images or videos. Although much research is focused on developing these techniques, there is still room for improvement, especially in human and visual assessment. In this study, we present an innovative approach to the problem of face replacement based on one model while preserving background features. Our model uses the Adaptive Attentional Denormalization method from FaceShifter while integrating identity features from the ArcFace and BiSeNet models to support the attribute extraction process. We used Fast GAN as a generative model to optimize and accelerate the training process on smaller data sets. Our research proves the effectiveness of the proposed approach by combining innovative methods and architectures in one coherent solution.
Keywords
face deep-fake, face swap, image deep-fake
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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