In the evolving landscape of artificial intelligence (AI), differentiating between authentic and artificially generated images poses a significant challenge, primarily due to the rapidly enhancing quality of AI-generated images.This paper systematically evaluates state-of-the-art classification models to distinguish authentic images from those synthetically produced using the CIFAKE dataset.We introduce FakeGPT and veuve ambal rose PFake, two new test datasets featuring genuine and AI-generated synthetic images with specific keywords paralleling the generation of the CIFAKE dataset.We use the transfer learning technique to train the state-of-the-art classification models on the CIFAKE training set, followed by rigorous evaluation against the CIFAKE, FakeGPT, and PFake test datasets.Further, we explore ensemble approaches, including stacking, voting, bagging, and meta-ensemble learning.
The culmination of our extensive research efforts is the Meta Ensemble eXplainable Fake Image Classifier (MEXFIC), which stands out with a notable accuracy of 94% and 96.61% against the Stable Diffusion generated CIFAKE and PFake datasets, respectively.This is a significant improvement over the tonic shower cap ConvNextLarge model, achieving the highest accuracy of 92.54% among the state-of-the-art models.Our study showcases the competitive edge of MEXFIC that highlights the necessity for more robust models capable of identifying AI-synthesized images, as evidenced by the performance on the challenging FakeGPT dataset.