Generative Adversarial Networks (GANs) have been widely used in various application scenarios. Since the production of a commercial GAN requires substantial computational and human resources, the copyright protection of GANs is urgently needed. In this paper, we present the first fingerprinting scheme for the Intellectual Property (IP) protection of GANs. We break through the stealthiness and robustness bottlenecks suffered by previous fingerprinting methods for classification models being naively transferred to GANs. Specifically, we innovatively construct a composite deep learning model from the target GAN and a classifier. Then we generate fingerprint samples from this composite model, and embed them in the classifier for effective ownership verification. This scheme inspires some concrete methodologies to practically protect the modern GAN models. Theoretical analysis proves that these methods can satisfy different security requirements necessary for IP protection. We also conduct extensive experiments to show that our solutions outperform existing strategies.

翻译：生成对抗网络（GANs）已被广泛应用于各类场景。由于商业级GAN的生成需要大量计算资源和人力资源，其版权保护问题亟待解决。本文提出首个面向GAN知识产权保护的指纹方案。我们突破了以往分类模型指纹方法直接迁移至GAN时存在的隐蔽性与鲁棒性瓶颈。具体而言，我们从目标GAN与分类器创新地构建复合深度学习模型，通过该模型生成指纹样本并将其嵌入分类器以实现有效的所有权验证。该方案为现代GAN模型的实用化保护提供了具体方法论。理论分析证明该方法能满足知识产权保护所需的多重安全需求，大量实验结果表明我们的方案优于现有策略。