mlsanityData to 7 Oct 2026

Fingerprinting Image-to-Image Generative Adversarial Networks

arXiv 2106.11760 ↗First posted 19 Jun 2021cs.CR cs.AI cs.CVcited by 1

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Abstract (arXiv)

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. This paper presents a novel fingerprinting scheme for the Intellectual Property (IP) protection of image-to-image GANs based on a trusted third party. 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 image-to-image translation GANs. 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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 35 of its 66 references that are in the map. First posted on arXiv 19 Jun 2021.

Builds on

35 of 66 references.

References · 35

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23 more
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Citing papers per year: 2023: 1.

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  1. 2023Deep Intellectual Property Protection: A Surveycited by 16