A Dataset and Benchmark for Copyright Protection from Text-to-Image Diffusion Models
arxiv(2024)
摘要
Copyright is a legal right that grants creators the exclusive authority to
reproduce, distribute, and profit from their creative works. However, the
recent advancements in text-to-image generation techniques have posed
significant challenges to copyright protection, as these methods have
facilitated the learning of unauthorized content, artistic creations, and
portraits, which are subsequently utilized to generate and disseminate
uncontrolled content. Especially, the use of stable diffusion, an emerging
model for text-to-image generation, poses an increased risk of unauthorized
copyright infringement and distribution. Currently, there is a lack of
systematic studies evaluating the potential correlation between content
generated by stable diffusion and those under copyright protection. Conducting
such studies faces several challenges, including i) the intrinsic ambiguity
related to copyright infringement in text-to-image models, ii) the absence of a
comprehensive large-scale dataset, and iii) the lack of standardized metrics
for defining copyright infringement. This work provides the first large-scale
standardized dataset and benchmark on copyright protection. Specifically, we
propose a pipeline to coordinate CLIP, ChatGPT, and diffusion models to
generate a dataset that contains anchor images, corresponding prompts, and
images generated by text-to-image models, reflecting the potential abuses of
copyright. Furthermore, we explore a suite of evaluation metrics to judge the
effectiveness of copyright protection methods. The proposed dataset, benchmark
library, and evaluation metrics will be open-sourced to facilitate future
research and application. The website and dataset can be accessed website
dataset.
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