Evaluating Convolutional Neural Networks for Synthetic Image Detection in the Frequency Domain

This paper evaluates the addition of Fourier Transforms into CNN models designed to distinguish real images from AI-generated ones, addressing challenges posed by the rise of hyper realistic content produced by AI. Unlike prior studies that apply frequency-domain analysis to general image classification tasks, our research specifically focuses on AI-generated image detection using the CIFAKE dataset. We implement a CNN architecture with Fourier Transform features to classify synthetic images. We hypothesize that combining frequency and spatial domain information improves CNN-based detection of AI-generated images. We ultimately disprove this through two experiments that show that our best-performing baseline CNN achieved a testing accuracy of 98.58 %, while our Fourier-based model reached an accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 8. 5 0 \%}$</tex>. Our findings highlight that incorporating Fourier features into the detection pipeline provides valuable insights, although the overall accuracy depends mostly on the CNN architecture. This research contributes to the growing field of digital authenticity by critically assessing the role of Fourier-based enhancements, demonstrating that spatial and frequency-domain integration does not necessarily yield superior results in detecting AI-generated images.