  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.
