Model-agnostic Origin Attribution of Generated Images with Few-shot
  Examples
Fengyuan Liu Haochen Luo Yiming Li Philip Torr Jindong Gu
Recent progress in visual generative models enables the generation of
high-quality images. To prevent the misuse of generated images, it is important
to identify the origin model that generates them. In this work, we study the
origin attribution of generated images in a practical setting where only a few
images generated by a source model are available and the source model cannot be
accessed. The goal is to check if a given image is generated by the source
model. We first formulate this problem as a few-shot one-class classification
task. To solve the task, we propose OCC-CLIP, a CLIP-based framework for
few-shot one-class classification, enabling the identification of an image's
source model, even among multiple candidates. Extensive experiments
corresponding to various generative models verify the effectiveness of our
OCC-CLIP framework. Furthermore, an experiment based on the recently released
DALL-E 3 API verifies the real-world applicability of our solution.