Level Up the Deepfake Detection: a Method to Effectively Discriminate Images Generated by GAN Architectures and Diffusion Models

• URL: http://arxiv.org/abs/2303.00608v1
• Date: Wed, 1 Mar 2023 16:01:46 GMT
• Title: Level Up the Deepfake Detection: a Method to Effectively Discriminate Images Generated by GAN Architectures and Diffusion Models
• Authors: Luca Guarnera (1), Oliver Giudice (2), Sebastiano Battiato (1) ((1) Department of Mathematics and Computer Science, University of Catania, Italy, (2) Applied Research Team, IT dept., Banca d'Italia, Rome, Italy)
• Abstract summary: The deepfake detection and recognition task was investigated by collecting a dedicated dataset of pristine images and fake ones. A hierarchical multi-level approach was introduced to solve three different deepfake detection and recognition tasks. Experimental results demonstrated, in each case, more than 97% classification accuracy, outperforming state-of-the-art methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The image deepfake detection task has been greatly addressed by the scientific community to discriminate real images from those generated by Artificial Intelligence (AI) models: a binary classification task. In this work, the deepfake detection and recognition task was investigated by collecting a dedicated dataset of pristine images and fake ones generated by 9 different Generative Adversarial Network (GAN) architectures and by 4 additional Diffusion Models (DM). A hierarchical multi-level approach was then introduced to solve three different deepfake detection and recognition tasks: (i) Real Vs AI generated; (ii) GANs Vs DMs; (iii) AI specific architecture recognition. Experimental results demonstrated, in each case, more than 97% classification accuracy, outperforming state-of-the-art methods.

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