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Watermarking for Stable Diffusion Models
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AI
DOI：10.1109/JIOT.2024.3434656.png)
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Abstract
En 中文
In the scenario of text data and image data interact of the Internet of Things (IoT) applications, the problem of copyright protection of the text-to-image models is threatened due to the replicability and portability of the neural network model. In order to solve this problem, we propose a model watermarking for the typical text-to-image diffusion models (DMs)-stable DMs (SDMs), which is a key aspect of the copyright protection of text-to-image models. Our scheme injects watermark into an SDM and makes the SDM generate watermark through a predefined prompt. The ownership of the SDM can be proved by the different output results of the model to the predefined prompt. The proposed method does not require raw training data and internal details of SDMs, which only need a predefined prompt and watermark to fine tune the pretrained SDM with minimal epoch. A large number of experiments show that our watermarking technology is effective, and can realize the copyright protection of the SDMs on the premise of less influence on the original function.
Keywords:
Copyright protection
deep learning
neural network watermarking
security
stable diffusion models (SDMs)
Copyright protection
deep learning
neural network watermarking
security
stable diffusion models (SDMs)
Journal
IF:
8.9
Papers:
1.4W
Citations:
7.8W
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Cited Papers
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