Fighting deepfakes by detecting GAN DCT anomalies
- URL: http://arxiv.org/abs/2101.09781v3
- Date: Mon, 15 Feb 2021 10:07:55 GMT
- Title: Fighting deepfakes by detecting GAN DCT anomalies
- Authors: Oliver Giudice (1), Luca Guarnera (1 and 2), Sebastiano Battiato (1
and 2) ((1) University of Catania, (2) iCTLab s.r.l. - Spin-off of University
of Catania)
- Abstract summary: State-of-the-art algorithms employ deep neural networks to detect fake contents.
A new fast detection method able to discriminate Deepfake images with high precision is proposed.
The method is innovative, exceeds the state-of-the-art and also gives many insights in terms of explainability.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Synthetic multimedia contents created through AI technologies, such as
Generative Adversarial Networks (GAN), applied to human faces can have serious
social and political consequences. State-of-the-art algorithms employ deep
neural networks to detect fake contents but, unfortunately, almost all
approaches appear to be neither generalizable nor explainable. In this paper, a
new fast detection method able to discriminate Deepfake images with high
precision is proposed. By employing Discrete Cosine Transform (DCT), anomalous
frequencies in real and Deepfake image datasets were analyzed. The \beta
statistics inferred by the distribution of AC coefficients have been the key to
recognize GAN-engine generated images. The proposed technique has been
validated on pristine high quality images of faces synthesized by different GAN
architectures. Experiments carried out show that the method is innovative,
exceeds the state-of-the-art and also gives many insights in terms of
explainability.
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