Image Prefiltering in DeepFake Detection

Szymon Motłoch, Mateusz Szczygielski, Grzegorz Sarwas

2022

Abstract

Artificial intelligence, becoming common technology, creates a lot of new possibilities and dangers. An example can be open source applications that enable swapping faces on images or videos with other faces delivered from other sources. This type of modification is named DeepFake. Since the human eye cannot detect DeepFake, it is crucial to possess a mechanism that would detect such changes. This paper analyses solution based on Spatial Rich Models (SRM) for image prefiltering connecting convolutional neural network VGG16 to increase DeepFake detection with neural networks. For DeepFake detection, a fractional order spatial rich model (FoSRM) is proposed, which was compared with classical SRM filter and integer order derivative operators. In the experiment, we used two different approximation fractional order derivative methods: first based on the mask and second used Fast Fourier Transform (FFT). Achieved results we also compare with the original ones and the VGG16 network with an additional layer added to select the parameters of the prefiltering mask automatically. As a result of the work, we questioned the legitimacy of using additional image enrichment by prefiltering when using the convolutional neural network. Additional network layer gave us the best results from the performed experiments.

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Paper Citation


in Harvard Style

Motłoch S., Szczygielski M. and Sarwas G. (2022). Image Prefiltering in DeepFake Detection. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-555-5, pages 476-483. DOI: 10.5220/0010841200003124


in Bibtex Style

@conference{visapp22,
author={Szymon Motłoch and Mateusz Szczygielski and Grzegorz Sarwas},
title={Image Prefiltering in DeepFake Detection},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2022},
pages={476-483},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010841200003124},
isbn={978-989-758-555-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Image Prefiltering in DeepFake Detection
SN - 978-989-758-555-5
AU - Motłoch S.
AU - Szczygielski M.
AU - Sarwas G.
PY - 2022
SP - 476
EP - 483
DO - 10.5220/0010841200003124