Enhanced Deepfake Detection Using Frequency Domain Upsampling

Mamadou Bah, Mohamed Dahmane

2024

Abstract

Recent advances in deep learning have dramatically reshaped the image processing landscape, specifically targeting the key challenge of detecting deepfakes in digital media. This study investigates the integration of Fourier or frequency domain upsampling techniques with deep learning models for the purpose of detecting deepfakes. Using the FF++ dataset as a benchmark for evaluating deepfake detection, our research rigorously evaluates the effectiveness of models such as Xception. This evaluation includes an in-depth exploration of various upsampling methods as well as the combination of spatial and frequency domain upsampling. The results reveal clear disparities in performance between the different models and techniques, and our experiments highlight the profound impact of the various upsampling and downsampling approaches on the accuracy of the resulting classification. Remarkably, combining the Xception model with upsampling and downsampling techniques increases detection accuracy by over 2%, while maintaining the constant input size inherent in the Xception architecture.

Paper Citation

in Harvard Style

Bah M. and Dahmane M. (2024). Enhanced Deepfake Detection Using Frequency Domain Upsampling. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 796-803. DOI: 10.5220/0012473700003660

in Bibtex Style

@conference{visapp24,
author={Mamadou Bah and Mohamed Dahmane},
title={Enhanced Deepfake Detection Using Frequency Domain Upsampling},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2024},
pages={796-803},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012473700003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Enhanced Deepfake Detection Using Frequency Domain Upsampling
SN - 978-989-758-679-8
AU - Bah M.
AU - Dahmane M.
PY - 2024
SP - 796
EP - 803
DO - 10.5220/0012473700003660
PB - SciTePress