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.

翻译：生成式AI（例如生成对抗网络 - GANs）近年来日益普及。然而，生成式AI在保护与所生成图像（及有毒图像）和模型（及中毒模型）相关的知识产权（IPR）与模型问责性方面引发了重大关切。本文提出一个评估框架，旨在全面概述GANs版权保护措施的当前状态，评估其在多种GAN架构上的性能表现，并识别影响其性能的因素及未来研究方向。研究结果表明，当前针对输入图像、模型水印及归因网络的IPR保护方法在广泛GAN架构下基本令人满意。我们强调，必须进一步关注训练集的保护，因为现有方法无法为训练集提供稳健的IPR保护与溯源追踪。