MEXFIC: A meta ensemble eXplainable approach for AI-synthesized fake image classification
Abstract: 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 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 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.
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