Generative AI (e.g., Generative Adversarial Networks – GANs) has become
increasingly popular in recent years. However, Generative AI introduces
significant concerns regarding the protection of Intellectual Property Rights
(IPR) (resp. model accountability) pertaining to images (resp. toxic images)
and models (resp. poisoned models) generated. In this paper, we propose an
evaluation framework to provide a comprehensive overview of the current state
of the copyright protection measures for GANs, evaluate their performance
across a diverse range of GAN architectures, and identify the factors that
affect their performance and future research directions. Our findings indicate
that the current IPR protection methods for input images, model watermarking,
and attribution networks are largely satisfactory for a wide range of GANs. We
highlight that further attention must be directed towards protecting training
sets, as the current approaches fail to provide robust IPR protection and
provenance tracing on training sets.
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