  The rapid advances in generative AI models have empowered the creation of
highly realistic images with arbitrary content, raising concerns about
potential misuse and harm, such as Deepfakes. Current research focuses on
training detectors using large datasets of generated images. However, these
training-based solutions are often computationally expensive and show limited
generalization to unseen generated images. In this paper, we propose a
training-free method to distinguish between real and AI-generated images. We
first observe that real images are more robust to tiny noise perturbations than
AI-generated images in the representation space of vision foundation models.
Based on this observation, we propose RIGID, a training-free and model-agnostic
method for robust AI-generated image detection. RIGID is a simple yet effective
approach that identifies whether an image is AI-generated by comparing the
representation similarity between the original and the noise-perturbed
counterpart. Our evaluation on a diverse set of AI-generated images and
benchmarks shows that RIGID significantly outperforms existing trainingbased
and training-free detectors. In particular, the average performance of RIGID
exceeds the current best training-free method by more than 25%. Importantly,
RIGID exhibits strong generalization across different image generation methods
and robustness to image corruptions.
