Alteration-free and Model-agnostic Origin Attribution of Generated
  Images
Zhenting Wang Chen Chen Yi Zeng Lingjuan Lyu Shiqing Ma
Recently, there has been a growing attention in image generation models.
However, concerns have emerged regarding potential misuse and intellectual
property (IP) infringement associated with these models. Therefore, it is
necessary to analyze the origin of images by inferring if a specific image was
generated by a particular model, i.e., origin attribution. Existing methods are
limited in their applicability to specific types of generative models and
require additional steps during training or generation. This restricts their
use with pre-trained models that lack these specific operations and may
compromise the quality of image generation. To overcome this problem, we first
develop an alteration-free and model-agnostic origin attribution method via
input reverse-engineering on image generation models, i.e., inverting the input
of a particular model for a specific image. Given a particular model, we first
analyze the differences in the hardness of reverse-engineering tasks for the
generated images of the given model and other images. Based on our analysis, we
propose a method that utilizes the reconstruction loss of reverse-engineering
to infer the origin. Our proposed method effectively distinguishes between
generated images from a specific generative model and other images, including
those generated by different models and real images.