Ownership Infringement Detection for Generative Adversarial Networks Against Model Stealing

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 to verify the ownership of a GAN. In this paper, we show that these methods are not robust to emerging model extraction attacks. Then, we propose a new method GAN-Guards which utilizes the common characteristics of a target model and its stolen models for ownership infringement detection. 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 achieves superior detection performance, compared to watermark-based and fingerprint-based 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, and adaptive attacks.