CGI-DM: Digital Copyright Authentication for Diffusion Models via
  Contrasting Gradient Inversion
Xiaoyu Wu Yang Hua Chumeng Liang Jiaru Zhang Hao Wang Tao Song Haibing Guan
Diffusion Models (DMs) have evolved into advanced image generation tools,
especially for few-shot generation where a pretrained model is fine-tuned on a
small set of images to capture a specific style or object. Despite their
success, concerns exist about potential copyright violations stemming from the
use of unauthorized data in this process. In response, we present Contrasting
Gradient Inversion for Diffusion Models (CGI-DM), a novel method featuring
vivid visual representations for digital copyright authentication. Our approach
involves removing partial information of an image and recovering missing
details by exploiting conceptual differences between the pretrained and
fine-tuned models. We formulate the differences as KL divergence between latent
variables of the two models when given the same input image, which can be
maximized through Monte Carlo sampling and Projected Gradient Descent (PGD).
The similarity between original and recovered images serves as a strong
indicator of potential infringements. Extensive experiments on the WikiArt and
Dreambooth datasets demonstrate the high accuracy of CGI-DM in digital
copyright authentication, surpassing alternative validation techniques. Code
implementation is available at https://github.com/Nicholas0228/Revelio.