A Difference-in-Difference Approach to Detecting AI-Generated Images
This addresses the challenge of reliable AI-generated image detection for security and verification purposes, representing an incremental improvement over existing reconstruction error-based methods.
The paper tackles the problem of detecting AI-generated images as they become increasingly realistic, proposing a difference-in-difference method that computes second-order reconstruction error differences to improve detection accuracy, with experiments showing strong generalization performance.
Diffusion models are able to produce AI-generated images that are almost indistinguishable from real ones. This raises concerns about their potential misuse and poses substantial challenges for detecting them. Many existing detectors rely on reconstruction error -- the difference between the input image and its reconstructed version -- as the basis for distinguishing real from fake images. However, these detectors become less effective as modern AI-generated images become increasingly similar to real ones. To address this challenge, we propose a novel difference-in-difference method. Instead of directly using the reconstruction error (a first-order difference), we compute the difference in reconstruction error -- a second-order difference -- for variance reduction and improving detection accuracy. Extensive experiments demonstrate that our method achieves strong generalization performance, enabling reliable detection of AI-generated images in the era of generative AI.