  Generative models, especially text-to-image diffusion models, have
significantly advanced in their ability to generate images, benefiting from
enhanced architectures, increased computational power, and large-scale
datasets. While the datasets play an important role, their protection has
remained as an unsolved issue. Current protection strategies, such as
watermarks and membership inference, are either in high poison rate which is
detrimental to image quality or suffer from low accuracy and robustness. In
this work, we introduce a novel approach, EnTruth, which Enhances Traceability
of unauthorized dataset usage utilizing template memorization. By strategically
incorporating the template memorization, EnTruth can trigger the specific
behavior in unauthorized models as the evidence of infringement. Our method is
the first to investigate the positive application of memorization and use it
for copyright protection, which turns a curse into a blessing and offers a
pioneering perspective for unauthorized usage detection in generative models.
Comprehensive experiments are provided to demonstrate its effectiveness in
terms of data-alteration rate, accuracy, robustness and generation quality.
