Detecting Discrepancies Between AI-Generated and Natural Images Using Uncertainty
Journal:
arXiv
Published Date:
Dec 8, 2024
Abstract
In this work, we propose a novel approach for detecting AI-generated images
by leveraging predictive uncertainty to mitigate misuse and associated risks.
The motivation arises from the fundamental assumption regarding the
distributional discrepancy between natural and AI-generated images. The
feasibility of distinguishing natural images from AI-generated ones is grounded
in the distribution discrepancy between them. Predictive uncertainty offers an
effective approach for capturing distribution shifts, thereby providing
insights into detecting AI-generated images. Namely, as the distribution shift
between training and testing data increases, model performance typically
degrades, often accompanied by increased predictive uncertainty. Therefore, we
propose to employ predictive uncertainty to reflect the discrepancies between
AI-generated and natural images. In this context, the challenge lies in
ensuring that the model has been trained over sufficient natural images to
avoid the risk of determining the distribution of natural images as that of
generated images. We propose to leverage large-scale pre-trained models to
calculate the uncertainty as the score for detecting AI-generated images. This
leads to a simple yet effective method for detecting AI-generated images using
large-scale vision models: images that induce high uncertainty are identified
as AI-generated. Comprehensive experiments across multiple benchmarks
demonstrate the effectiveness of our method.
