Generative adversarial networks (GANs) have shown remarkable success in image
synthesis, making GAN models themselves commercially valuable to legitimate
model owners. Therefore, it is critical to technically protect the intellectual
property of GANs. Prior works need to tamper with the training set or training
process, and they are not robust to emerging model extraction attacks. In this
paper, we propose a new ownership protection method based on the common
characteristics of a target model and its stolen models. Our method can be
directly applicable to all well-trained GANs as it does not require retraining
target models. Extensive experimental results show that our new method can
achieve the best protection performance, compared to the state-of-the-art
methods. Finally, we demonstrate the effectiveness of our method with respect
to the number of generations of model extraction attacks, the number of
generated samples, different datasets, as well as adaptive attacks.

提出了一种基于目标模型及其盗版模型共同特征的新型所有权保护方法，无需重新训练目标模型，能够有效抵御现有模型提取攻击和自适应攻击，并在实验中取得了最佳保护效果。