Marra et al. performed an interesting study in order to detect unseen types of fake generated data. Concretely, they proposed a multi-task incremental learning detection method in order to detect and classify new types of GAN generated images, without worsening the performance on the previous ones. Two different solutions regarding the position of the classifier were proposed based on the successful algorithm iCaRL for incremental learning: i) Multi-Task MultiClassifier (MTMC), and ii) Multi-Task Single Classifier (MT-SC). Regarding the experimental framework, five different GAN approaches were considered in the study, CycleGAN, ProGAN, Glow, StarGAN, and StyleGAN. Their proposed detection approach, based on the XceptionNet model, achieved promising results being able to correctly detect new GAN generated images.