  Over the past years, images generated by artificial intelligence have become
more prevalent and more realistic. Their advent raises ethical questions
relating to misinformation, artistic expression, and identity theft, among
others. The crux of many of these moral questions is the difficulty in
distinguishing between real and fake images. It is important to develop tools
that are able to detect AI-generated images, especially when these images are
too realistic-looking for the human eye to identify as fake. This paper
proposes a dual-branch neural network architecture that takes both images and
their Fourier frequency decomposition as inputs. We use standard CNN-based
methods for both branches as described in Stuchi et al. [7], followed by
fully-connected layers. Our proposed model achieves an accuracy of 94% on the
CIFAKE dataset, which significantly outperforms classic ML methods and CNNs,
achieving performance comparable to some state-of-the-art architectures, such
as ResNet.
