Deep Classifiers and Wavelet Transformation for Fake Image Detection

Authors

  • Stanislaw Osowski Warsaw University of Technology, Poland https://orcid.org/0000-0003-3194-4656
  • Maciej Golgowski Military University of Technology, Poland

DOI:

https://doi.org/10.26636/jtit.2023.4.1336

Keywords:

continuous wavelet transform, convolutional neural networks, deep fake, ensemble of classifiers

Abstract

The paper presents the computer system for detecting deep fake images in video films. The system is based on applying
continuous wavelet transformation combined with the ensemble of classifiers composed of a few convolutional neural networks of diversified architecture. Three different forms of forged images taken from the Face-Forensics++ database are considered in numerical experiments. The results of experiments on the application of the proposed system have shown good performance in comparison to other actual approaches to this problem.

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References

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Published

2023-10-31

How to Cite

Osowski, S., & Golgowski, M. (2023). Deep Classifiers and Wavelet Transformation for Fake Image Detection. Journal of Telecommunications and Information Technology, 4(4), 1–8. https://doi.org/10.26636/jtit.2023.4.1336

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