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arXiv · 2004.10448

DeepFake Detection by Analyzing Convolutional Traces

Luca Guarnera·Oliver Giudice·Sebastiano Battiato
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Abstract

The Deepfake phenomenon has become very popular nowadays thanks to the possibility to create incredibly realistic images using deep learning tools, based mainly on ad-hoc Generative Adversarial Networks (GAN). In this work we focus on the analysis of Deepfakes of human faces with the objective of creating a new detection method able to detect a forensics trace hidden in images: a sort of fingerprint left in the image generation process. The proposed technique, by means of an Expectation Maximization (EM) algorithm, extracts a set of local features specifically addressed to model the underlying convolutional generative process. Ad-hoc validation has been employed through experimental tests with naive classifiers on five different architectures (GDWCT, STARGAN, ATTGAN, STYLEGAN, STYLEGAN2) against the CELEBA dataset as ground-truth for non-fakes. Results demonstrated the effectiveness of the technique in distinguishing the different architectures and the corresponding generation process.

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Luca Guarnera, Oliver Giudice, Sebastiano Battiato. 2020-04-22. DeepFake Detection by Analyzing Convolutional Traces. https://arxiv.org/abs/2004.10448

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