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The image deepfake detection task has been greatly addressed by thescientific community to discriminate real images from those generated byArtificial Intelligence (AI) models: a binary classification task. In thiswork, the deepfake detection and recognition task was investigated bycollecting a dedicated dataset of pristine images and fake ones generated by 9different Generative Adversarial Network (GAN) architectures and by 4additional Diffusion Models (DM). A hierarchical multi-level approach was thenintroduced to solve three different deepfake detection and recognition tasks:(i) Real Vs AI generated; (ii) GANs Vs DMs; (iii) AI specific architecturerecognition. Experimental results demonstrated, in each case, more than 97%classification accuracy, outperforming state-of-the-art methods.

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