Ownership Protection of Generative Adversarial Networks. (arXiv:2306.05233v1 [cs.CR])

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 …

adversarial critical emerging gan gans generative generative adversarial networks intellectual property making networks ownership process protect protection training