DeepFake Detection by Analyzing Convolutional Traces

• URL: http://arxiv.org/abs/2004.10448v1
• Date: Wed, 22 Apr 2020 09:02:55 GMT
• Title: DeepFake Detection by Analyzing Convolutional Traces
• Authors: Luca Guarnera (1 and 2), Oliver Giudice (1), Sebastiano Battiato (1 and 2) ((1) University of Catania, (2) iCTLab s.r.l. - Spin-off of University of Catania)
• Abstract summary: We focus on the analysis of Deepfakes of human faces with the objective of creating a new detection method. 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. Results demonstrated the effectiveness of the technique in distinguishing the different architectures and the corresponding generation process.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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