  The rapid progress of generative AI technology has sparked significant
copyright concerns, leading to numerous lawsuits filed against AI developers.
While some studies explore methods to mitigate copyright risks by steering the
outputs of generative models away from those resembling copyrighted data,
little attention has been paid to the question of how much of a resemblance is
undesirable; more original or unique data are afforded stronger protection, and
the threshold level of resemblance for constituting infringement
correspondingly lower. Here, leveraging this principle, we propose a
genericization method that modifies the outputs of a generative model to make
them more generic and less likely to infringe copyright. To achieve this, we
introduce a metric for quantifying the level of originality of data in a manner
that is consistent with the legal framework. This metric can be practically
estimated by drawing samples from a generative model, which is then used for
the genericization process. Experiments demonstrate that our genericization
method successfully modifies the output of a text-to-image generative model so
that it produces more generic, copyright-compliant images.
